So thank you for joining this session. This is, my name is Mark Baker. I'm joined by John Casey who is the founder and CTO of C-Play Networks. And we're just gonna spend the next half hour or so talking to you about a particular use case that we had with a carrier out in Asia where we combine our technologies to deliver a very compelling solution to the customer. John's actually gonna be doing most of the hard work and talking as he has with the great familiarity with the case. Before we do that though, I was going to just give you a couple of kind of intro slides to set the scene. So I'll reference, I work on the OpenStack team at Canonical, so working on Ubuntu OpenStack. So we have seen increasingly with the telcos carriers and enterprises that we've been engaged with over the last year that one of the drivers of adoption, one of the drivers actually of success of deployment of OpenStack is very much how you are able to operate the cloud. So many people will know Ubuntu as being a free technology. You can go and download, who uses Ubuntu? Far from Ubuntu. Good, good, so a lot of people use Ubuntu. You probably just went on to Ubuntu.com, downloaded it and used it. This free software is great, isn't it? But the cost of operating free software as software has increased in complexity has increased itself. And so trying to address that problem, Ubuntu is well known for usability, trying to address that problem, we develop sets of tools and methodologies and processes to ensure that you're able to operate complex software at scale in a cost efficient way. And that's really the success that we're going to be talking about today. So in the telco world, this means you have to address some very complex concepts. So if you're in the telco world, you've probably heard about Etsy. This is a standards body that defines how tools and technologies can address the requirements of a telco stack in a standardized way so that vendors like ourselves and others in the ecosystem can try and address pieces of that. And so when we're looking at how we automate this, how we drive the cost of operations down, we map our product sets against the requirements or the definitions that exist in this Etsy model. And this ensures that if you're a carrier like PCW or like Deutsche Telecom or like some of the others that we're engaged with in this space, you can use our tools and technologies and know that if you ever wish to, for whatever reason, there are alternatives out there you could choose, and you're not going to have to completely re-architect your entire environment. So our tools and technologies map against this very closely. The other piece is that operations, right, it's about, yes, maintaining the cloud, keeping it up, keeping it stable, understanding audit and compliance and those things, but it's also about upgrading. And this slide is really to show you that even though PCCW are deployed on Metaka, right, they know that they will need to upgrade to newer versions fairly soon to take advantages of the new features that exist because which is the best version of OpenStack, right? The best version of OpenStack is the one that delivers the features that your business needs. And that's generally going to get better and better with each release. And so being able to maintain upgrades or manage upgrades without downtime, without even API downtime, which is what we're able to do now is very important for these operators. And so if you have any questions about any of the pieces on here, I'm not going to drill into any of these features and how they relate to OpenStack in this talk. I'm more than happy to on the side if you wish. If you have any questions, please come and grab me afterwards or come and talk to us on the booth. But now I'd like to hand over to John who's really going to drill down and if we can flip the machines, he's really going to drill down into PCCW's use case and how we tackle that and how we addressed it. Thanks, Tom. Thank you, Mark. Thank you for joining the session as Mark said, John Casey, CTO and co-founder of Cplane Networks. So if you guys don't know who PCW Global is, they're a worldwide service provider, one of the largest service providers. And they're part of an $8 billion business globally. They are in 135 countries and over 3,000 cities worldwide. They own their own fiber. They're part of a consortium of fiber. They provide connectivity services, MPLS services worldwide across the globe, strong in Middle East, Asia, but also in the US and in Europe. So building the cloud for them. They started about 18, 24 months ago. Why? Why would a telco who's worldwide do this? Well, typically because telcos are going through a transformation these days, right? They're trying to reduce costs. They're trying to provide new services. They're trying to virtualize. And the way that PCCW went about this was a very smart way. They said, they looked out in the market and they said, we're gonna start with coming off the shelf products. We're gonna focus on doing what's easy first, you know, kind of cutting their teeth on the telco, the telco services. And, you know, this is a journey for them, right? And so 24 months into it, they're fully virtualized. And they picked a handful of vendors. We're one of them and there's some others in the room. This is in contrast. And I think that what they did was pretty smart in that, you know, most telcos can handle this type of transformation. If you look at what AT&T did, right? They hired thousands of developers and they forked a lot of code and put, you know, five million lines of code in open source. Now there's not too many telcos that can swallow that type of thing. So I think that this is certainly more palatable than to most telcos. So they're key goals, right? They wanna build a global cloud, a broad use cloud that they can leverage for short-term and long-term services. A globally connected cloud to basically reduce the cost of their operation, both CAPEX and OPEX. And then provide services, virtualized services for both their internal use services for their customers and also provide certain services, virtualized services for their customers as well. So in terms of their use cases, you know, provide a generalized cloud connecting to the common AWS model or Azure model, start going down the network function virtualization markets, extending their MPLS services with SD-WAN, provide virtual CPE to their customers, extending both to their enterprise businesses, the retail businesses, and also new markets and smart cities and whatnot. Then the fourth use case is quite interesting because once you have a globalized cloud, you can start thinking about moving functions that you would normally do in a central data center out to the edge. Edge analytics is a really interesting use case because now that they can collect data at the edge, they can process data at the edge, they can create the knowledge at the edge and then they can tune their network at the edge and then they can just bring some of the low knowledge back to the central location for analysis, later analysis, as opposed to huge back calls of data that they would normally bring to their back. And then also they wanna enable new services like IoT or gaming to their customers. So we were challenged about 24 months ago. They said, okay, we want to build, base our technology on OpenStack and containers, but we want to build not just one in a data center, we want from 10s to potentially thousands of clouds, worldwide. Anywhere we have a point of presence, we want potentially a cloud, right? And we want to establish distributed tenancy, right? So our customers or the applications we're deploying, we want those applications to be tenant knowledgeable across this entire globe, right? So if there's 30 locations that a customer has, that they have services is they want that distributed tenancy to extend. And because they're worldwide, they have a latency problem, right? So I think the average latency from somewhere in Hong Kong to somewhere in the US is around 260 milliseconds. So thinking about the problem of one compute node in Hong Kong and one in, let's say, Chicago, that's a long, long latency, right? So how do you deal with that latency? And then integrate with their existing BSS and OSS systems is represented in another challenge. Functionally challenged, they wanted to have a unified view. They wanted a global inventory of those services or their tenancy of their customer applications. So they want to be able to have, you know, understand where all these applications are for a given customer to be able to move them, migrate them, build them. And then they want to be able to say, location placement of functions. So the edge analytics case, I know I have a pop in some GPS coordinate. I want to find the best location within a few milliseconds of that pop to do my edge analytics. So I want to be able to decide based on GPS coordinates where to place these functions. And then so they also want to be able to support different types of applications. So NFE, customer virtualization, IOT services, a cord, that sort of stuff. Across this, they want an infrastructure that would support all of these application types. And then metadata is very important to just about everything in this world. And then they want to basically exhaust everything that they do, they want to exhaust metadata. They want to have metadata repositories where they can analyze everything that they're doing on the network. So how do we solve this problem? Well, first of all, keep it simple, right? Deploy individual OpenStack instances in a sharding method, so share nothing. Everything is individual. And that solves a major problem of areas of responsibility, areas of maintenance. So this really was in line of, they had different organizations around the world managing their OpenStack services so they could just fit them very easily in that operational model. And this also allows for specialization of hardware. So in some cases, they can deploy one version of OpenStack with DBDK, for example, for NFE. And then they can play around with accelerations of for machine learning, they could deploy another OpenStack either in that region or another region. And then for distributed tendency across the globe, they used a routed layer three, both VXLAN turning into MPLS, right? So that utilizes their existing MPLS network, very easy to integrate VXLAN into that. It also is compatible with their current network network model. And it's a well-known, well-understood technology that doesn't have any complex edge cases. And then in order to provide the metadata in order to provision all of these services, basically orchestrate OpenStack from the top, right? Metadata describes each OpenStack instance, what technology they have, what hardware they have. The customer relationship complexity is abstracted above OpenStack and the knowledge of where a customer is is abstracted above OpenStack so we can have this distributed tendency across. The APIs then to provision OpenStack are very similar to OpenStack APIs, but their purpose built for a customer and that's distributed tendency model. Let's see, unlimited scale, Billy. So what does this look like from a topology perspective? So we have, you see, we have, here's an example of two OpenStack environments connected with two tenants connected through an NFV solution that extends, you see the OGR, basically extends the customer VXLAN inside OpenStack into an MPLS VRF. This is how we get edge to edge tendency across the globe. And we also have the ability then to push that tendency in that VRF into an AWS context or even to a VCPE context up at the top, right? So we have the knowledge of each OpenStack environment, how that relates to the MPLS world and then into AWS and the virtual CPE. They use Mass and Juju for zero day deployments. This was basically reducing their deployment time of OpenStack from days to two hours, really. Our products are fully integrated, OpenStack is fully integrated, Mass and Juju. Additional applications and services like SAF and other of the key components are integrated into Mass and Juju. And it really did eliminate the mass of people needed to deploy OpenStack down to a few people across the globe. So there's a big win for them. And it also allows for rolling upgrades of OpenStack. So kind of the key SDN features that we provided within OpenStack, I'll just highlight a couple here. First of all, being able to traffic shape floating IP. So being able to reduce floating IP to they can reduce it down to 100 meg or five meg on a given tenant or a given VM. The ability to have a known app floating IP. So things like SIP gateways, you need the same IP address in both internal and external. So we can provide that within a VXLine context. L2 and L3 forwarding of course, service function chaining of course for VNFs. And the OGR capability is our VNF that extends VTOP through BGP into a VRF context. And then the ability to integrate with physical switches. So the virtualization is great but there's still a physical world out there. And the ability to extend a VTOP from a compute node into a physical switch is really important. Particularly on border gateways. Jump in here and just talk about our deployment of our controller model. So we have an L2, L3 plugin that sits on the neutron node. That sends topology events into our controller. Our controller will essentially create a tenant context or a tenant topology where that tenant has been deployed on compute nodes and kind of isolate that tenant, the understanding of that tenant to basically push flows down to the compute nodes. We have an agent that sits on the compute nodes that then pushes flows into OVS and things like DHCP services. So we have localized DHCP services. Basically it's a complete sharding model. So we don't try to distribute data across all the compute nodes which allows it to scale. And then we have a, as part of the tenant context, we launch this VNF service as part of the initialization. This allows us to extend this VXLAN into a VRF on a pertinent basis. And as well as being able to provision VRFs on a pertinent basis to extend those VXLANs across the LAN. And then we also push NetConf and CLI into the top erect tours. Here's an example of what the physical topology looks like. We take floating IP and SNAT into the compute nodes. We have an OGR node that goes to a MPLS network. We've got standard Nova services, SEF services and our multi-site manager capability, which then provisions over the top of OpenStack. So the multi-site manager, again, is the technology that provisions. It's an orchestration technology that sits above OpenStack that basically shards OpenStack instances and the data for OpenStack instances across all of the OpenStack instances. So it's a knowledge base for tenant context across OpenStack instances. It allows us to deploy applications or workloads across these tenant contexts. It does all the provisioning. When you attach a user into a site, it does all the provisioning of that user, Keystone, the project, the VRFs, the VNFs to extend the VXLAN, all the networks, the floating IPs. It pushes all that into OpenStack. So there's zero touch needed to provision a customer into multiple sites in OpenStack. It's very high throughput, so this can handle thousands of compute nodes, sorry, thousands of OpenStack instances, and it allows a geolocation service. So every OpenStack instances coded with a geolocation and the millisecond latency from every other OpenStack instance in the world. So it allows us then to do specialized placement of functions and applications. In terms of NFV, we've integrated the multi-site manager with RIFT-IO or the OSM model for RIFT-IO. So the multi-site manager has the global context or the global topology of all the OpenStack context. The OSM or the RIFT-IO then has the catalog network services so then you can use that combination of catalog of network services and multi-site to basically deploy multi-site network services across the globe. We use RIFT for the MANO and MSM. We use Juju for the zero day and day one configuration and then we have some technology that does day two and beyond network service orchestration. So here's an example of the multi-site manager deploying the virtual CPEs. So when you deploy a virtual CPE, you ship a box to a customer. That customer then will need a, you turn down that box, it'll register with a provisioning server. The provisioning server will then give, download the software into that box. The multi-site manager provides that context and then from there on, we can push through the OSM MANO, we can push NFV solutions to that box. And then we can connect that box through either the MPLS network or private line networks, central office NFVs to the NFVs out at the customer sites. So I'm gonna do a quick demo. So this is, I'll turn on a video and walk you through the video, but this is basically our multi-site manager. What we're gonna do is we're gonna provision two open-stack environments and we're gonna launch services on this, we're gonna launch services across two open-stack sites. So let's think about this application as, okay.  So what we'll do in this demo is we'll go ahead and create a customer context first of all, call it NUCO. And that's information about that NUCO is held centrally in the multi-site manager. You can add some attributes to it, how many sites that they're deployed in or wanna deploy in. And then we're gonna go attach that customer to two sites. So now we're gonna go provision open-stack and push the context of this NUCO into two sites. So we'll just set up some network parameters. These will be turned into API calls that'll then push down from the multi-site manager into we'll call basically Neutron that will spin up the open-stack networks VXLAN. So now you see the API is going down the side and we're instantiating. So what this is doing is it's instantiating that customer in Keystone, two Keystones. We're creating the projects, we're creating all the necessary objects in open-stack and the networks, the floating IP networks, the internal networks and the MPLS networks. So we've already peered with the PE router in an ASN context within that VXLAN context. So now we're gonna add some external sites. So if you already have some AWS has been set up, we can then peer to a BGP context within AWS. So we'll just put in the parameters and the ranges of both the overlay technology. So basically this will be an over-the-top network. Okay, so that's where we reach AWS. We're also doing Azure. Let me just fast forward this here. Now what we're gonna do is we're gonna add some BGP entries so that they're basically like ACL so that we can extend the BGP context across AWS, Azure and our open-stack sites. Now we're gonna create some VMs. So in this case, we're using a GUI to create VMs, but in the general case, you'd probably create a Tosca document and then just as part of that Tosca document have some sort of site location that you deploy applications. So we're gonna create a couple of VMs here. And we're gonna attach those VMs to different network interfaces in these two sites. And then we're gonna also, set some floating IP quotas, right? So we're gonna rate limit the floating IP for some of these VMs. So that's just issuing a Nova boot, right? And the green means it's been started and you have full control from our APIs to do whatever you want with the VMs. You can even look at the console, start and stop. We just fast forward this here. So this is what it looks like inside open-stack. So that we've actually done the provisioning. And I'm gonna cut this short. Mark, did you wanna do a demo for here? I think we have about six or seven minutes left. Thank you. Thank you, John. So one of the, the tool that John was showing there, which is a Cplane network multi-site manager. Have I got that right? Yes, that's right. Good. Now I've forgotten where I wasn't typing my password. But it's nicely integrated. And this is one of the things I wanted to show you here. It's nicely integrated into our environment. And so this environment, this is the tool that we use to model open-stack. And this is the logical model of open-stack. You'll see that there are a number of the open-stack services here. So we're gonna choose one of them. It's Glass, for example, or Nova Compute. And the other piece is, this is the GUI logical modeling tool that we use, and we can then apply that actually to a physical environment. So we'll see the physical machines that we have associated with this, the open-stack services, and indeed the Cplane services running, Cplane network services, I should say, running inside containers. And I'm showing you this actually because they're running on these two orange boxes that you can see down here at the front, which is running a full Mataka-based open-stack cloud. This one right here with Cplane attached. And so if we go and drop into the Horizon dashboard, you'll see a number of different VMs that we're running. And this is all nicely integrated with the multi-site manager from Cplane. So if I was to launch a VM, in fact, just gonna do it, right? A little dangerous, I'm now going off script. There we go. Reload, boom, boom, this is always what happens when you go off script. But if I wanted to launch a VM, then that would, you'd see that come up and then be reflected in the multi-site manager with the network attached in the right way. So we've done some great integration with these tools to make it very easy for the telcos to operate. And this was the reason why PCCW and others are working with us and Cplane networks because it's not standing up open-stack, right? Most people can do that today, right? It's not even necessarily integrating a VNF, most people, sorry, an SDN technology or networking technology, most people can do that today. It's doing it in a way that allows you to repeat this deployment and operate it in a scalable, efficient manner, right? And that means tight integration between the tooling that we, Canonical, have done and so have Cplane networks to deliver value to PCCW. So I think we've just got a couple of minutes. Three minutes. Three minutes, if you had any questions about this, then we'd be happy to take on-topic questions and much more likely to be answered. No? Well, Bill, yes sir. I'm sorry? Sure, we're a software company based in Silicon Valley. Been around since before, around about 2013. We have about 20 employees worldwide and we have a architectural platform, a platform for orchestrating, it's a broad-based platform for orchestrating many, many things. All of our products are built on the same platform. We have an SDN solution, as you saw. We have VNFs to connect to MPLS. We actually have hardware provisioning, basically provisioning of hardware through CLI and NetConf and all that. The ability to provision MPLS and we have a multi-site manager. And we do extend applications above on this platform. Thank you. A lot of them are operational. I'm not allowed to talk about them in detail. I can talk about them in broad brush, right? So, but everything that we talked about is in some form of operation. Great, if there are no further questions, then we will, I promise, have this up and running on our booth. We're right inside the hole. First on the left is you're walking through the hole. So, if you want to see this in more detail and kick the tires and try it out, please do come to the booth. Do you play networks? Also have a booth on the show floor. The detail. Yes, so they can also talk at length about this. And if you'd like to know any more about how you can use this or about the PCCW implementation, then we'll be happy to talk about it. Thank you. Thank you.


Thank you very much, it's a great pleasure to see all the friends here and okay so I don't need to introduce myself. As you can see from the title this paper is about the study of data privacy regulation. So we want to examine the impact of a general data protection regulation. GPR was enacted in 2016 and entered into effect in May 2018. It is widely recognized as a tough privacy and data security law in the world. And after it's thrown out becomes a blueprint for privacy regulation in many other countries and states with a very similar regulation in California, the so-called CCPA. So the key rule in GDPR is the requirement of consent from the data subjects as stated in GDPR and a freely given specific informed and unambiguous indication of the data subject to wishes. So that's the key rule and we are going to examine how this rule is going to affect the market. So after two years of its rollout in 2020 the European Union in formal paracation claims that there is a great success of GDPR. So as I mentioned the GDPR has been an overall success meeting many of the expectations. Unfortunately there's disagreement from economists and there's lots of imperial studies record its adverse effects. For instance there's a wide evidence showing that a total number of cookies therefore the number of data collected actually dropped a lot and that is going to affect the platform's revenue according to Johnson's estimation by 50% and also estimated by John and others find that the European countries the venture capital investment would drop 26% comparing with the United States. As also people would argue that GDPR compliance costs a significant level of resources and eventually they will be passed on to consequence. So clearly there is a disagreement between regulators and economists and such kind of disagreement actually raises several fundamental questions for economists to think of and to study. The first question we want to address is how do we reconcile the conflict between consumer data acquisition and privacy protection? Second question on what economic basis do we evaluate the overall impact of regulation? Third also very important so what is optimal regulation optimal mechanism of data acquisition that maximizes social welfare? So this paper aims to answer these questions and I hope I can answer these questions. So in this paper I present a new model for the analysis of consumer data acquisition and privacy regulation. So before addressing this feature model I would like to rethink the economics of consumer data. So we all know that consumer data has become a fundamental resource for the modern digital economy but according to current business model consumer data is actually treated as a free good. The existing business model for data acquisition is very simple free service for free data that is digital platforms provide free digital services or contacts for consumers and when you browse your website generated consumer data and then they can collect for free and then monetize from data. So that is a business model of course there's common sense behind this business model economists or businessmen would argue that because the data acquisition costs zero marginal cost digital platforms mainly invest a large amount in digital infrastructures that's fixed cost however the marginal cost of collecting data and processing data is almost zero so then this data should be priced at zero yeah but this common sense is wrong because it does not count the cost of data acquisition on consumers data acquisition generates a negative externality of consumer privacy and consumers incur a private cost when digital platforms harvest their data so this is very important and actually this feature is going to turn over completely the concept of free data. So now I would like to briefly introduce some key modeling features of this paper first of all I would consider a concept of private cost harvesting consumer data causes a private concern and this concern can be regarded as a loss of utility of a cost from the point of view of economics and it is also nature it's natural to think that private cost increases with amount of data being collected I call this data scale but what is also important is private sensitivity and attitudes actually are subjective and idiosyncratic that is we can imagine that consumers are heterogeneous in their privacy sensitivity so this is a very important assumption and actually this assumption is motivated by some experimental evidence and empirical tests which I not cited here cited here of course the concept of private cost is also used in several other papers but it is treated as homogenous in most papers so introducing heterogeneous privacy sensitivity is a key difference from other papers so our second modeling feature is to introduce data analytics the existing literature treated mostly or focused mostly on processed data and regarded as an informative signal for product pricing and recommendation here we try to distinguish from this method knowledge by separating the data and the process as two essential inputs as we all know that raw data does not have much value and there's a capability of processing raw data to extract available information it's called the data analytics actually data analytics is an like umbrella term that incorporates lots of activities including data capturing data processing data storing data analyzing and also like interpretation of data but what we needed to emphasize is it is not simply like a software like people think about machine learning actually it is exploratory undertaking such as research and development and the investment in data analytics becomes the most important asset for most digital platforms so consider this data analytics now with this new concept we can consider both raw data and the data analytics as two essential inputs and the combination of these inputs generates a revenue to digital platforms and benefit to consumers so in the baseline model we take a reduced form for the revenue and the benefit functions but we do discuss the micro foundation of these functions now we have we can characterize the benefits of the data we can also now discuss the costs so digital platforms build the investment costs for data analytics while consumers incur private costs for data provision so with this methodology we can identify the benefits and the cost of data acquisition and we can construct a social welfare function that is equal to the aggregate benefit minus the aggregate cost so our methodology is very simple we examine the impact of regulation based on the comparison of social welfare function so let me briefly introduce the baseline model there's a digital platform as a representative platform who provides free digital services because we do not focus on this service this service so we assume the cost of providing the service is zero and there's a continuum of consumers so with total population equal to one and each consumer derives utility u from using digital service we assume that u is sufficiently large so from data collection digital platforms earn a per consumer revenue which is called r s and fee from data collection and a consumer derives additional benefit b here s is the measure of data scale we harvested from each consumer and the fee is data analytics so you can think about revenue as a bad revenue generated from online advertising and a consumer benefit may be the benefit from the improved experience in online activities or personalized services digital platforms can control the data scale by scale by varying the number and types of cookies okay so on the cost side now digital platforms build the investment cost called i of fee which is convex and the consumers incur private cost s of theta so here s is the scale and the theta is the key parameter notes the privacy sensitivity and is distributed according to a cdff we assume theta upper is not too large to ensure full participation so consumer now derives a gross utility is u of s fee theta equal to little u that is the utility from using free digital service plus the extra service capital v of s v theta here capital v of s v theta is the net surplus from data sharing benefit minus cost and we also needed to impose the regular assumptions like concavity and for we also denote capital b as the per consumer social benefit that is the revenue plus consumer benefit a key assumption is we assume s and fee are compliments that is partial derivative cost derivative is positive so that could be interpreted as the relationship between data analysis and data scale because the better data next allows for larger data scale and of course the more data can improve data analysis okay so with this construction of the cost and benefit i'm going to introduce a benchmark for welfare comparison so we consider first the best as a benchmark suppose a social planner runs a digital platform and has completed information about theta so the social planner chooses a type contingent to scale as of theta and transfer to maximize total social welfare the total social welfare is equal to the integral you can see from the bracket so b of s theta fee minus s theta theta is the net surplus from net social surplus from data acquisition while there's also the investment cost of i and fee so the first best data scale is very simple you can see that actually it's the balance of marginal benefits of data to the marginal cost and the first best data analysis can be recognized in a way now i should pause here and see if there's any questions no question good just can ask one quick clarification so is it important in your in your model that this utility of consumers that does not depend on phi because in practice you could imagine that you know consumers don't necessarily care about the firms having the raw data but about the fact that the firm can interpret the data and and learn stuff about them that's you of you you mean you uh yes yes you have s s times theta something like that and i was wondering why you don't have that's the cost oh that's cost yeah so you mean the private cost uh he has supposed a specific analysis i just see that but in general we can consider quite a general cost of function but uh it says analysis is quite messy yeah i should consider extension thank you very much and sit you yeah so when you compare the the social planner and the firm the social planner is allowed to offer a menu of options and yes you don't see to allow the the firm to offer a menu of options you know more privacy at a higher price for example you will see actually i will consider this many options so it performs okay thank you very much okay so so with this simple setting we can discuss the impact of GDPR first it's quite natural we can see actually there's a market failure before GDPR because before GDPR digital platforms just bundle free service with data acquisition and digital digital platforms pay zero price for digital for data acquisition as a result platforms chooses uh choose s and fee to maximize its profit uh here we assume a full participation of consumers before GDPR and uh clearly digital platform will collect as much data as possible that's the upper bond of the data scale uh there's over collection of data uh this over collection of data causes consumer welfare laws the surplus the extra surplus from data collection could be negative for consumers with high privacy costs as the overall the gross utility still could be positive because we assume you is sufficient large that is in order to use this free digital service actually consumers with high uh private costs incur a loss as they incur negative surplus another effect is excessive data could lead to excessive investment in data analytics because they are complementary okay so uh there in mind there's a market as a market failure before GDPR now we are going to address the role of GDPR so under GDPR digital platforms must have a consumers consent to collect data as under this requirement platforms are required to unbundle with digital service uh from the default to consumer consent so uh according to GDPR platforms must allow users to access their service even if users refuse to allow data collection this is called the GDPR up out but further if consumers allow data collection through non-essential cookies we call GDPR opinion they enjoy additional benefits be or maybe extra compensation from the platforms as they incur a private cost so with this option consumers with high privacy sensitivity they will up out but they can still use digital service and uh we we can see yeah yeah one comment is that uh you assume that quality of service is the same whether i accept or i reject but because i get the feeling that in practice they have various ways to degrade quality of services if you refuse like to pop up or i mean i pop up every five minutes so yeah yeah yeah yeah so very important comment i was worried about this point but i checked the GDPR website at least according to GDPR uh this regulation it's illegal to degrade the service but uh then we can see that actually uh the platform will upgrade service for open consumers as a compensation so we can see that with this consent requirement actually GDPR opens a door to fix a market failure because now GDPR entitles consumers to trade personal data for extra benefit of course they needed to incur extra cost and the consumers will do so if extra benefits exceed the privacy cost and in order to attract more consumers to opt in now digital platform needed to compensate consumers for data acquisition as we know mentioned that they needed to upgrade services for open consumers offer personalized services so in addition to BSV maybe they want to offer some kind of extra transfer we call TV so many digital platforms now provide a uniform option for accepting all cookies for opting when you log in their website they are accepting or or not if you accept then you can use the extra service so this is uh called the uniform policy so we will first analyze this uniform policy we assume that s and t where t is the transfer so under this uniform policy consumers will opt in if the extra benefit plus b is greater than uh cost as theta so this determines a cutoff of the threshold for participation as called tau so with this tau we can write write down digital platforms profit as equal to f of tau times b of sv minus s tau minus i of f is the investment cost so that is digital platforms need to compensate each opting consumers uh with a cost equal to the private cost of the marginal time and that retains the net social benefit from uh data sharing so uh digital gdpr actually changed consumers default choice and under the gdpr all consumers are secured a non-negative surplus from data sharing up how the consumers receive zero opting consumers receive positive surplus which is equal to uh it's true uh private cost the cost between its true private cost and the price cost of marginal time and so we can just use the first order conditions to solve the uh the characterized equilibrium and uh clearly the equivalent data scale is in dodginess determined by balancing the marginal social benefits with the marginal cost of the type of the marginal type tau and uh so digital platform collects less data from opting consumers this further increases consumer surplus uh so based on this uniform policy we are going to just compare the the impact of the gdpr while we we can clearly see that consumers are better off but uh consumer up how to actually reduce this the equilibrium data analytics and uh because the investment cost can be recouped only from opting consumers so this further leads to a lower data scale due to the complementarity to s and p such kind of reduction can be quite significant i will uh will generate a negative impact on data analytics and what's the evidence from the venture capital as mentioned by ja uh you can see uh will be quite significant about 30 percent so the wrong run negative effect on data analytics is a real concern uh now as i mentioned uh in the first best uh sorry benchmark so uniform uh data policy is not optimal uh because we have had to join as consumers so quite a nature uh quite a natural we needed to think about next design approach actually what we found that many digital platforms like premium leaga and prolific they uh already started offering menu of options of cookies so consumers when you click they will give you a menu you can click different types of cookies uh this this is a menu according to your preferences so we are going to characterize the optimal mechanism which is a kind of menu option but suppose digital platform offers a type dependent data scale s theta and transfer t theta of course the digital platform does not know the value of theta there is a private information and uh so there's a standard ir constraint and i i see constraint sorry okay and uh uh we can use a standard max design approach to characterize the platform's profit uh as uh after uh some calculation we can rewrite as a platform's profit as equal to w df theta minus i theta tf w is the net social benefit from a type theta consumer uh comparing with uh the first best you can see the extra term extra cost which is type c that's the information right uh so here uh a profit pi is equal to the social welfare function so that indicates that is the optimal uh policy uh actually maximize the total social welfare and the asymmetric information so by solving these first out conditions we can characterize the second best contract a second best policy we call the s double theta and d theta uh they are characterized as first out of conditions uh so this uh optimal mechanism design approach actually uh can generate some kind of important policy implications uh so first of all we can see that gdpr established a set of principles but uh they do not provide the detailed guideline and in order to implement the second best there is a long way to go uh in particular according to the current gdpr compliance policy you can see actually there's uh different cookie policies can take different forms uh most digital platforms do not provide the detailed specifications on different types of cookies even uh some platforms offer many of options but consumers do not understand how much and what kind of personal data will be collected so a particular cookie so of course choosing an option of cookies is not as straightforward as choosing mobile phone and so in this paper we are going to uh propose uh some kind of guidelines for for the implementation of so a second best the most important one we would suggest is to characterize and standardize cookie specifications uh for instance a cookie a non-essential cookie specification should include the variety of data being collected the volume of data being collected the purpose of data how data is going to be analyzed and the value of data uh actually uh coincided with my idea actually later I found that Apple indeed introduced a similar kind of concept they called privacy levels in December 2020 uh they required the Apple developers to disclose their data collection by filling out a so-called nutrition levels this is a similar idea to the nutrition facts levels for the packaging you can see maybe you are most worried about calorie and protein similar so from the option or website you can find that the option form actually defines 14 data types 32 specific data items and six data usage uh further Apple also classified three categories of purposes like data are used to track you data link to you data not linked to you so data so Apple's movement actually is an important endeavor to standardize specifications of data features but there is one limitation because its design does not allow a manual publishing so this contributes to implementing its optimal uniform policy but not the second best but we expect Apple can improve this in the future uh okay so with limited time I can only introduce some one or two simple uh extension applications or one important impact or significant impact of GDP is on the third party cookies third party cookies are generated by external domains totally by pricing platforms and this after GDP has rolled out several dominant uh browsers like Safari and Firefox they have brought a third party cookies recently Google announced its movement to block all third party cookies in 2003 so this movement of course is going to have a significant impact on online pilots and according to uh empirical concept estimation that could reduce revenue by 50 but we think it's also important to examine the welfare effect of consumers so I use a variant of the baseline model to address this issue and try to capture the two features of third party cookies first third party cookies can track consumers across different websites therefore it causes a higher private cost than first party so I assume the private cost is gamma s theta with gamma greater than zero versus s theta for the first party cookies second digital platforms do not bear the cost of data analytics but they share a third party profit from selling ads so with this very simple setting we can understand that before GDPR the digital platform does not bear the cost of data collected by the third party but can share the cost so of course it will practice maximum data and it does not care about consumers private concern but after GDPR the digital platform needs to compensate consumers for the private cost caused by the third party cookies so now they face a cost benefit trade off but small platforms they don't have their servers or the demand side platforms they still rely on third party to generate revenue from a private but the dominant platforms like Google they will face out the third party when the private concern is significant significant enough but we find that actually facing out the third party ads increase consumer surplus of course here's the main concerns Google could further enhance its power from this movement as they're needed to balance I also consider data acquisition with personalization but I don't have time to discuss this and actually the baseline model can be easily extended to incorporate personalized pricing and the results are quite similar and policy implications are similar now so with about two or three minutes left let me briefly discuss the rate of literature so there's a growing literature of data collection and data intimidation as yeah I just need to list several like as mogul beg my and the harsh is excellent work and they treat the processor data as information as a signal and their study provides very important solid micro foundation but here we separate the data and the processing as two strategic inputs and emphasize the different nature of benefits and cost from these inputs they also treat consumer privacy costs as a reduced form of utility and assume homogeneity across consumers we consider heterogeneous consumer privacy cost also the most important relation is that with Detroit and the co-authors excellent paper but we have different modeling features and policy implications they consider heterogeneity of privacy sensitivity on different types of personal data for instance maybe consumers are more concerned about their income data than address as they find is that excessive data collection before GDPR is mainly driven by data externality and also argue that monetary inducement for opting should not be allowed we consider heterogeneity of privacy sensitivity across consumers and so we do not consider data externality but we find that excessive data collection is caused by a marked failure due to bundling and we show that the monetary transfer plays an important role in fixing massive data and also characterize the segment as health so concluding remarks so we develop a new model to analyze consumer data acquisition and privacy regulation and we have two features we treat both data and that actually a separate input so consider heterogeneity of privacy cost so we identified market failure before GDPR and it reveals GDPR's role in fixing market failure so the limitations we do not consider data externality as choice paper and other second we do not consider heterogeneity of privacy sensitivity on different types of personal data as choice okay thank you very much that's the end of my presentation stop sharing thank you shijun now we have a discussion by jpv joy jpv okay so it's a great pleasure to discuss issues in the paper on privacy so it has a very clean analysis with policy implications for an important topic so given the limited time allocated to me let me highlight some technical aspect of the model that are crucial for the for the main results after my discussion i'm sure that other people can also chime in for policy implications so in my comments are somewhat technical so let me use a slide for to facilitate my discussion okay so okay so can you see the slides yes okay so so one crucial assumption of the paper or the basic premise of the paper is that the platform is kind of additive so services are provided for free these assumptions are fine because there are so many other finance platforms and that they are very very important however since the main research are driven by this assumption we need to understand the what role it plays and why so to summarize so services are provided for free and in addition the value of this service is just sufficiently large so what are the implications that are all consumers are subscribed to the service even with the maximum data collection so in other words there is no extensive margin so the market is essentially covered and without regulation then this automatically implies that too much collection of personal data because of privacy and what happens with regulation such as a gdpr where gdpr requires opt-in so the platform now needs to compensate to induce a consumer opt-in so basically the role of regulation in the model is to create a market for personal data and in this model there is some an easy asymmetric treatment of a basic utility and the extra utility so the gross utility has two components so one is a basic utility and the other one is extra utility and in the paper all actions are taken place in this extra utility term so this appointment has also raised earlier by Bruno so the assumption is there is some basic utility nothing can be done on this component so you cannot charge any price and also you cannot adjust the quality depending on the amount of collected data so all actions are coming in this component so now then one question is why nothing can can nothing can be done on this component because for instance if the business model is different and the services can be charged then what happens and also what if the basic utility can be can be adjusted depending on the amount of data collection okay if that is allowed that essentially creates a market for privacy so in they can say actually even without regulation we are going to have exactly the same result okay so so let me go to this point a little bit more and by the way with regulation actually what's happening is that there's a two-liter collection of data and this is due to the expensive back in other words the marginal consumer in this model is has actually the highest sensitivity among consumers who are up in okay so that means that average consumer the sensitivity is lower than the marginal consumer so that automatically leads to two-liter data collection so this can be actually pointed out in the in the panel so what if the service is not provided for free okay so then as I mentioned earlier okay so even without regulation we have a price mechanism that will induce the same result as a regulation so then we can imagine a suggestion where actually data collection is gives too much advertising revenue for the platform so actually a platform may want to actually charge a negative price to induce more consumers to up the e okay but we couldn't we can have a suggestion we are actually not negative so negative price cannot be charged so in other words the price cannot be negative so then the price mechanism does not work anymore and then we have a suggestion uh akin to the suggestion of a no regulation in in june the model so here now we have two different interpretations of the model so in june the model so he considers no regulation as the kind of no property right for the consumer and then with regulation now consumers have the right to to to their data so in a sense the role of regulation is to which party to give a property right to to to data so that's the one I wanted to think about so I mean I'm actually taking looking at this this suggestion from a different angle more like a quotient perspective so we know that as long as there is a efficient contracting the allocation of a property right does not matter in the in the overall final allocation so basically as long as the price is the price mechanism is there so actually we are going to have exactly the same problem and then the whole issue becomes uh the non-existence of uh uh market or not well defined property property right that's I mean another way to look at it so that's the main main comments and there are also some small minor comments so every person's individual and this has been covered by and the second point is actually raised by Alex earlier so the assumption is that consumer price causes to depend only on data collected but how data is processed I think the main qualitative results we will go through but I saw in the paper there are a few results that actually depend on this this assumption so also one thing to think about is here data analytics investment we can also think about what kind of investment okay some investment may just try to enhance consumer experience or some analytics investment maybe more focus on how much revenue to derive okay so there could be also what type of uh data analytics investment to be done this can be also something to be discussed and also uh timing in the personal price model I mean this model has not been presented by Zhijian but there was just a little bit uh a strange assumption in the timing so they are actually consumers were allowed to opt out after receiving a personalized offer if I understand it correctly but personalized offer is based on data collected so there seem to be a little bit of time inconsistency in the search so these are some some some uh minor comments okay


As-salamu alaikum brothers and sisters. Sending the reward of Umrah to your departed loved ones or those unable to undertake the journey themselves is a profound expression of love and devotion. At Pure Passage we specialize in performing Umrah on behalf of your sick or deceased family members, ensuring they receive this sacred gift. We understand the challenges and impossibilities some face in embarking on this spiritual journey. The message is here to alleviate the physical and financial burdens, offering a professional and reliable service that takes care of every detail. Let us help you fulfill this obligation for your loved ones with utmost care and attention. Make it happen today, contact Pure Passage and secure this immense reward by performing Umrah on behalf of those close to your heart. Be Ethni Allah. Alright guys, welcome back to the channel if you're new, my name is Bobby Geis. By now we should all know that pork is bad for you, but the reality is of course that people worldwide still consume it, especially Hong Kong is amongst the top consumers of pork in 2023. With plenty of studies pointing towards pork being detrimental for your health, being full with parasites and worms and whatnot, but people simply do not want to understand this simple truth. Even back in the day, in the old ages, we had scriptures, the Torah, the Bible and finally the Koran. All of those scriptures warn against pork, but still the Christians do not want to hear anything about it. So today guys, we're going to have a concise list of four points why pork scientifically is bad for you. Guys before we jump into the video, if you enjoy my work leave me a thumbs up, subscribe to the channel if you haven't already and check out the links in the description box below to further support. And now with no further ado, let's have a look. Alright, number one, absolutely shocking and this is something that I never heard about, hepatitis E. Here we read, in developed nations, pork liver is the top food-based transmitter of hepatitis E, a virus that infects 20 million people each year and can lead to acute illness, fever, fatigue, jaundice, vomiting, joint pain and stomach pain. The acute illness can even lead to an enlarged liver or sometimes liver failure and even death. Yeah, I don't think bacon is worth it man. So now what's really important for pork eaters is of course how alarming are the pork contamination stats. In America about 1 out of every 10 store-bought pig livers tests positive for hepatitis E. Guys this is absolutely insane man, we're not talking about slight contamination, some by-product, no we're talking about hepatitis E and 1 out of 10 pig livers tests positive for hepatitis E in America, disgusting. The rate is 1 out of 15 in the Netherlands, 1 out of 20 in the Czech Republic. And in Germany, that's where I grew up, they found out that 1 out of 5 pork sausages were contaminated with hepatitis E. So as you can see this does not only apply to people that eat pork liver, nor to simple German pork sausages.  So there you can see that every single pack therefore must have contamination of hepatitis E. How absolutely repulsive this is. The same applies to France as well, in France you have traditional pig liver sausage or raw sausages, they call them saucisson and therefore they find that half the local population shows evidence of hepatitis E infection. I guess pork is just too yummy. The article continues we can find the same hepatitis E concerns in Japan and in the UK too. But what is really hilarious is that people just cannot fathom to quit eating pork. No instead of just quitting pork, they come up with tools and tricks. Apart from total pork abstinence, the best way to slash hepatitis E risk is in the kitchen. For virus deactivation, cooking pork products for at least 20 minutes to an internal temperature of 71 Celsius or 160 Fahrenheit seems to do the trick. Ok, not eating pork would be just too easy. Number 2 and even more shocking, multiple sclerosis. I worked in a hospital back in the day and I saw patients that had multiple sclerosis, such an unbelievably detrimental sickness. Young people in their 20s with MS, absolutely devastating and now we find out that pork consumption can actually lead to MS. I have never heard that before but apparently here we read that this was known since the 1980s. The robust link between pork and MS, multiple sclerosis, has been known at least since the 1980s, when researchers analyzed the relationship between per capita pork consumption and MS across dozens of countries. While pork-averse nations like Israel and India were nearly spared from MS degenerative grips, more liberal consumers such as West Germany and Denmark faced sky high rates. As I said, yes, I worked in a hospital in Germany and therefore I saw young people with MS. What would have been interesting here is of course to show Muslim countries how high is MS within Muslim countries. This year is very fascinating as well, we read the potential for pig brain to trigger nerve-related autoimmunity isn't just observational hunch either. Between 2007 and 2009 a cluster of 24 pork plant workers mysteriously fell ill with progressive inflammatory neuropathy, which is characterized by MS-like symptoms such as fatigue, numbness, tingling and pain.  So look how disgusting and fascinating this is at the very same time.  So do you understand now why you shouldn't slaughter pigs? Do you understand now why you shouldn't come near them at all? Your Bible states the very same thing. Leviticus 11.7 and the pig, for though it divides the hoof, thus making a split hoof, it does not chew the cut, it is unclean to you. More detail we find in Leviticus 11.3.28. The pig has a split hoof divided in two but doesn't chew the cut and so it is unclean. You may not eat their meat nor touch their carcasses. They are unclean to you. So here within the Bible we find a warning against touching the carcasses, the dead bodies of pigs. Now scientifically you can understand why. Not only the touching process but being around the slaughter process itself is enough to potentially give you multiple sclerosis. Number three, liver cancer and cirrhosis. Liver problems tend to trail closely on the heels of some predictable risk factors namely hepatitis B and C infection, exposure to aflatoxin, a carcinogen produced by mold and excessive alcohol intake. This basically sums up the diet of the Westerner, pork meat and alcohol. And then everybody is wondering why do we get heart disease, why are we getting sick? Here we can read for decades poor consumption has faithfully echoed liver cancer and cirrhosis rates around the world. In multi-country analysis the correlation between pork and cirrhosis mortality clocked in at 0.4. And this has been examined since 1965. We have data from 1965, from the mid 70s and from 1996. Very important to note as well is that beef by contrast remained liver neutral or even protective in these studies. We are not going to make science our god but it is still very interesting to see of course. Muslims eat beef, they do not eat pork. The Christians eat pork, the Hindus on the other hand do not eat beef. But here in this scientific study we can find that beef in contrast remained liver neutral or even protective. So you tell me who is on the right side. Number 4. Yersinia.  For years pork's precautionary motto was well done or bust. Which yet again means you have to cook it to the ground in order to consume it in the first place. Why would you? If you look for example at sashimi, fish or you look at steak tartare, beef, you can eat it raw. No problem whatsoever. But of course you cannot do that with pig, with pork. Why is that so? Because it's riddled with parasites. That's a parasite? That is a parasite. It's so gross. Look you can start, you can see them starting to crawl the way out. This one's moving. Oh gross. I am never eating pork again. A consequence of fears about trichinosis, a type of round worm infection that ravaged poor consumers throughout much of the 20th century. So if hepatitis wasn't enough and liver cancer, now you can get some ringworms on top of that. Still love bacon though.  But its long term consequences are what should really ring alarm bells. Victims of your senior poisoning face a 47 times higher risk of reactive arthritis, a type of inflammatory joint disease triggered by infection. When children become post your senior arthritis target, sometimes requiring chemical cyanovectomy, the injection of osmic acid into a troubled joint to relieve persistent pain. Yeah well, that doesn't sound alarming at all. So now let's read the conclusion. The jury is still out for two of pork's problems. Hepatitis E and your senior aggressive cooking and safe handling are enough to minimize the risk. Oh well. And due to a shortage of controlled pork-centric research capable of establishing causation, pork's other red flecks spring from epidemiology, a field rife with confounders and unjustified confidence. Worse, many diet and disease studies lump pork together with other types of red meat diluting whatever associations might exist with pork alone. These issues make it hard to isolate the health effects of pig-derived products and determine the safety of their consumption.  Alright, that's it for today's video. After reading this article you had four extremely strong points. Hepatitis E, liver cancer and what not, tapeworms. They did not even mention parasites but nevertheless in the end they come to the conclusion. You know what guys? As long as you cook your pork it's a-okay. Just go for it. Feed it to your children who cares about the potential arthritis, right?  Let them have bacon. It is so much more important than anything else. Guys, point of the story is every single scripture. The Torah, the Bible and the Koran warn of pork. Now people say, well, we are secularists.  We do not believe in those fables. This is just a fantasy book. Alright, fine. Then look into your science that you worship as a God. The science points out pretty clearly here that pork is extremely bad for your health. It's just what it is. You can see it clearly there. But then they still gonna come up with excuses and try to find a trick. Well if you heat it over 70 degrees that should do the job. If you look into the data, if you look into foodborne illnesses, you look into viral infections, you look into parasites, tapeworms, etc. It is always pork. Now they need to narrow it down further. Of course you cannot conduct such a study.  We need to do a pork exclusive study. Let's just take a bunch of people and feed them only pork. Eat pig only for a whole year and then we can determine truly if it was the pig or not. Because the people that eat pig, they eat beef as well. They eat chicken as well. So how would we potentially know? We have absolutely no clue, right? Man, this is absolutely ridiculous of course.  Like that you can compare. You can see if for example multiple sclerosis is running wild within Muslim countries or not. Like that you can narrow it down. But ultimately people don't want to do it. There is this funny saying that states it is easier to change a man's religion than to change his diet. And this truly holds true. I know this from back in the day during my vegan times or even as a fitness trainer back in the day. I saw it. It was the hardest thing for people to simply stop eating processed junk. There's so much emotional attachment to foods. Guys, face the truth. Pork is bad for you. You don't have to convert to Islam even though it would be better for you. Of course, simply stop eating pork, man. It is poisoning you. Replace the bacon with beef bacon. Alright guys, but this is it for today's video. If you liked it, leave it a thumbs up, subscribe to the channel if you haven't already and check out the links in the description box to further support. And now, and as always, may God bless you all, much love and peace.


What's going on everybody? Got Kyle and Sean here. We're ripping it personal. This one's for Mr. Tom's seat Got nine boxes. I've got four boxes of Gilded Four boxes, Bowman Mega, one Cosmic, and one Bowman Chrome hobby. Good luck here, Mr. Tom. Four Bowman Megas Gilded Cosmic Chrome Ten boxes. Cool. Let's do it, Tom. Good luck my man. We'll start with the Bowman Megas. Bowman Megas No, it'll be tomorrow. It'll be tomorrow. We are full for tonight We are maybe leaving over full, but if you do order it now, it'll rip tomorrow night right around this time Maybe a little bit earlier But we are full for tonight my man. Goat Schmidt, Yuri Perez Rookie, Garrett Cole, and Dort I'll see rookies in these base packs. Most are looking for the silver packs over here Bailey, Miguel Cabrera, Morrell, Jordon, Garrett Mitchell, Langlears I'm all in, Tom. See some fire, man. We've done it before. Let's do it again So silver packs here Mega boxes. Got a first John Sock shim One is Otto Samuelsavala on the Choice Auto for the Padres On card Sammy got iconic of Machado, Brooks Brandon, and Juan Benjamin First up Edwin Solano got Jesus Galiz and pink is gonna be Nelson Rada for the Angels number to 199 Brooks Lee and Sammy Zavala Go Tom See some fire out of here my man Story Ruiz Pena KB Devers Gunner Alonzo Julian And guys, I hope everybody did have a fun night me and Sean tonight if you did enjoy the stream Please do hit that like button guys Over on the YouTube if you guys are on YouTube Helps us out quite a bit Say Yasser Mercedes on the choice Cam Collier, Miguel Blis, Brooks Lee, and Sammy Zavala Got a yellow say first Yossi Galan for the Rangers Roman Anthony and yellow is Hendry Chavilly on the first one's number to 75 yellow choice Twins Brooks Brandon and a Juan Benjamin box number three Corbin Carroll Bryce Parfa Signs Dealing allegations Michigan Michigan stealing signs. What's up with that in person scouting is that is that illegal or against the rules? True waters. Oh, Michigan.  There's a Rainer Arias another nice one Sepeda Caminero and Sarmiento One is Lantigua on the first got a Elyon Soto It's iconic of trout Dominguez and Jayce Young All righty, Mr. Tom one more mega One more bowman mega then we got some gilded some cosmic and bowman chrome hobby all in Tom Julian Joey Weimer Garcia Riley green for 80 seya Michael Harris Wander and Joey Ortiz now for our mojo packs And a first felon in Salisthen nice one there some nice silver pack hits Gennaro Miller got iconic trout Drew Gilbert and Jason Churio Marcello Meyer. There's a first Oxyoplas Ludwig Espinosa Brady house and Michael Arroyo All righty then Mr. Tom that I'll do it for the bowman mega's now. We'll do a bowman hobby Bowman chrome hobby Yeah, again guys, we do have release day tomorrow going to be noir basketball We'll have Lane over on fanatics live starting at 10 and then we'll have Sam over here starting at 11 Starting at 11 still got the break schedule link there in the chat if you guys want to check it out You can see what time everything's going down tomorrow Sam coming in at 11 and then four so be coming in Starting at five So we got spots for sale on our website as well as fanatics live as well Have a lane coming in 10 a.m. Green shimmer. It's a gunner iconic and green shimmer to 99 is going to be a jace young Tigers young prospect I think that's Josh's brother Josh young Heston Kirstad or Roman Anthony There's a fuchsia vapor is gonna be first. You'll injury Vargas nice one there Tom to 199 You'll injury the good prospect for the Dodgers their shortstop prospect I'm on super somewhere in here Super somewhere in here Jeremy Pena iconic Cam Collier Michael Harris. It came to the league rookie Francisco Guerrero say auto Clark Elliott's a 499 that'll be for the A's Clark Elliott on the auto alien Soto. I'm on Tom. There's a nice brand of my yeah It's a 99 on the green shimmer. I don't work the Yankees stop prospect Couple nice numbered ones here keep it going my man. Let's keep it going Cosmic and then we're on to gilded Aqua ray wave is going to be Mark Vientos for the Mets to um, 199 On the rookie Machado. There's my yeah Yankees Yankees spent a bag on my yeah, man A couple million for a young young player Hey, it came to the league Spencer Jones. There's Guanipa for the Braves First Chavili and Javier Rivas Alrighty there Tom that'll do it for the bowman chrome now. We got some tops chrome cosmic Good luck my man. We will end with the gilded Good luck Tom got a kodai sang at a 199 nice aqua shimmer rookie There he is badard Yeah, they did they're not a very good team. That's why they got badard. Yeah, that's very true They're not a very good team There's a Corey seager et star clusters for the Yankees on the young players and jt romuto I think it's a lottery too. So like they weren't the worst last season But they ended up winning the lottery and getting them There's a scope rookie Cal Stowers that works out Tommy Edmond on the refractor JD Martinez Manoa It's a Bryce Harper Cal Raleigh There's another star clusters triple Come on Pluto still haven't seen a Pluto Rookie Riley green on the ET Sean Murphy pink volley rookie There's a launch into orbit schwarbaum launched two into orbit the other night Not tonight though A little cold tonight Yo sheet on the rookie base DL Hall say clusters for the blue jates To 99 is John Carlo for the Yankees And the blue poll star price Harper ET Refractor is Boba shit Tyler Freeman priority almost gilded time Tom Come on come on to 75. We got salvi Perez on the green um green uh speckle speckle That's a mini diamond. That's a speckle got a rookie Kodai Senga ET scope is Taylor Ward Star clusters for the Rangers young and seager Another launch into orbit is poo holes the machine The last pack on the cosmic It is supernova. It is wander on the uh Case hit supernova's Wanda Not who we want not who we're looking for Alrighty guys four to go got gilded now for you mr. Tom. Good luck Let's see some bangers here, man. Let's see some bangers Box one. Well, let's finally see that cut auto See that cut auto for mr. Tom in our frame To 75 we got a wave Miguel Vargas Dodgers JT brown muto number to 25 on the ray weight and first auto is simian woods richerton the 75 twins These cards look so good, man and a chris morelle the 99 rookie for the cubs Really really good-looking cards, man. Come on. There we go to 25 gold frame rookie is adley ruchman Boom there we go mr. Tom That'll pick it up nice one there to 25 on the gallery of gold. We'll mag that Adley 22 at 25 very nice hit my man adley clutchman box number two Come on. Tom. Let's keep it going So we're from two different cases. So let's see some fire. Very nice one there my man Let's do it again. Let's do it again. A mini diamond of 50 of von Herrera rookie for the cardinals Got a francisco alvarez nice one there to 75 on the wave rookie Mets and leopold piguero gonna be our auto here to 75 Pirates rookie and a riley green to 99 rookie on the base May good rookie boxer in our frame Is hull of frame a hall of fame frame to 99 dave winfield For your Yankees this one there on the hall of fame gold frame Dave winfield in 99 I got you I guess it's not a nt right box three here tom Come on Speak that adley Speak that adley luck my man So we got a way to 75 rookie max mire for the marlins Got a or a satchel page for the st. Louis browns to 10 on the lava It's a throwback there st. Louis browns. I haven't even heard of that And auto is michael massy to 75 rookie for the royals And a christian yellied in 99 on the base maroo crew This one is hall of fame auto to 15 other gold frame is jeff bagwell This one there hall of famer from the stroves back in the day Nice one on card to 50 And last box here mr. Tom Box number four of gilded ready mr. Tom appreciate your order sir last box So you waved a 75 logan a hoppy rookie for the angels a as well cabrera that one is a um It's a 25 a lava array wait very wait It's one yankees rookie And is equal to ron rookie for the rangers to 99 On the on-card auto and a spencer tokelson to 99 on the base tigers The last one here It is hall of fame gold frame auto is pudge rodriguez to 99 Nice little pudge there on the rangers really nice auto as well Signs it clean That'll do it for you tom on the pudge close us out. Thank you sir 10 boxes for you mr. Tom Really nice hit on that adley out of um gilded my friend We will get these right out to you as always. Thank you sir