No Priors Ep. 53 | With AMD CTO Mark Papermaster

Key Takeaways

hi listeners for potential AI Founders my early stage AI fund conviction is accepting applications for its embed accelerator for two more days embed offers $150,000 in an unapt safe more than half a million of free compute and API credits a hand selected set of peers and access to Leading founder and research mentors apply at ed. conviction. by March 1st hi listeners and welcome to another episode of no priors today we're excited to be talking to the CTO of AMD Mark papermaster Mark has had a storied career in chips and Hardware with previous leadership positions at IBM apple and Cisco we're excited to have Mark on to get into gpus and the competition that's been driving this industry welcome Mark thanks s glad to be here with you and elad can you start by telling us a bit about your background you've worked on all sorts of interesting things from the iPhone and the iPad to like the latest generation of AMD super Computing chips oh sure I've been around a while so what's really fun is my timing was pretty good getting into the industry as a electrical and computer engineering grad University of Texas and got really interested in chip design and so it was back at a time when chip design was radically changing the kind of Technology everyone uses today seos was just coming into uh you know production usage and so I got uh on IBM's very first seos projects and created some of the first design so I got to get my hands dirty and do just about every facet of Chip design and had a number of years at IBM and took on different roles uh took on driving the microprocessor development at IBM across of first their power uh PCS and that was you know meant working with apple and Motorola as well as the Big Iron the the big Computing chips that we had in the Mainframe and in the big RIS serers so uh really got all facets of Technology there and included working on some of their server development but then uh shifted over to um uh to Apple uh Steve Jobs hired me in to run the iPhone and iPod uh and so I was there for a couple years but it was a a time of a great transition in the opportun in the uh industry and and for me it was a great opportunity because I ended up in 201 fall 2011 uh taking the role here at AMD of being both CTO uh and and really running the Technology and Engineering and right at a point where Moore's laws starting to slow down and so you know tremendous Innovation was needed yeah I want to get into that and sort of what we can expect in terms of computing Innovation if we're not just dramming more transistors on chips or we're unable to do that um every one of our listeners I think has heard of AMD but can you give like a very brief overview of the major markets you serve there sure so AMD is a a storyed company it's been around a well over 50 years and it it started out really being you know a Second Source company really bringing uh you know Second Source on key components and x86 microprocessors but you fast forward to where we are uh today uh and it's a very very broad portfolio uh when uh Lisa and Sue our CEO and I were brought uh into the company just over 10 years ago uh it was with a a mandate to uh get uh AMD back into very very strong competitiveness and so uh we started with the CPU line brought the CPU uh very very competitive and then really across the portfolio and just in February of 2022 acquired xyl link so that expanded the portfolio further so AMD creates the world's largest supercomputers it's got a massive install base now in the cloud so many of your Cloud operations that you're running are running on uh AMD epic x86 CPUs gaming were we we're huge we're underneath all the Xbox all the PlayStation as well as many uh gaming devices that uh uh that that you buy when you buy your your uh add-in boards and then across uh embedded devices with all of that rich zyink portfolio as well as embedded X8 six and we we acquired Pando so it extends that portfolio uh right into a networking interconnect that we need as we as we scale out these workloads so very very broad portfolio yeah AMD has had a pretty amazing run over the last decade plus since you joined um one of the things that you folks have really emphasized over the last couple years as well as Ai and there's been a big shift both in terms of the adoption of AI over the last decade or so in terms of traditional uh CNN RNN and other types of um neural net networ architectures but also in terms of this shift of Transformers and diffusion models and everything else um can you tell us a little bit more about what initially caught your attention in the AI landscape and then how AMD started to focus more and more in that over time and what what sort of solutions you've come up with you bet well uh we all know the AI Journey you know has been going since uh really the the the race began when uh the application space for AI opened up uh and gpus were viously uh pivotal there when you look at the uh the the key work that uh you know uh Hinton had done in terms of showing how gpus could drastically improve the uh accuracy of image recognition natural language processing uh and so that that that's been known uh for some time and so what we did at AMD is uh we uh right away saw the opportunity uh the question was plotting our course uh to be that strong player in AI so it was a very uh thoughtful and Del deliberate strategy because AMD we had to turn around the company so if you look at where AMD was uh in uh you know 2012 uh you know through uh you know really 2017 uh it was largely all all of the revenue was based on PCS and then gaming and so it was about making sure that the portfolio the building blocks were compe those building blocks had to be leadership they had to attract people to uh get on that AMD platform for high performance applications and so first we actually had to rebuild the CPU road map and that was a Zen microprocessors that uh that we released in 2017 uh in both uh PCS with our ryzen line as well as epic our x86 server line so that started the revenue ramp for the company and and started extending uh our portfolio and so right about uh that time uh in parallel as we saw where heterogeneous Computing was going we had we had called the ball on H hetrogeneous Computing before uh myself before lease ever joined the company uh AMD had made a a great acquisition of ATI that brought GPU into the portfolio it's one of the big reasons I was attracted to uh to AMD uh in the role is that wow what it was one of the it was the really the only company that had uh a very strong CPU portfolio and a very strong GPU portfolio and to me it was clear that the industry needed that powerful combination of the serial the scaler competing of these traditional CPU workloads and the massive parallelization that you get from a GPU uh and so we started with that heterogeneous compute uh created an architecture around that so we've been shipping CPUs and gpus combined for p applications longer than anyone started shipping those in 2011 with what we call apus accelerated processor units and then for Big Data applications we started with HPC the kind of high- performance compute technology that's in National Labs it's in oil exploration companies and so uh we uh focused first with the you know big government bids that ended up leading to supercomputer wins that we now have AMD uh CPU and AMD gpus under the world's largest supercomputers but that work started years ago and it was equally a hardware and a software effort uh and so uh we've been building that hardware and software capability and it really culminated in December 6 of 20123 of last year when we announced our Flagship the Mi 300 which just is a beast for both a high performance compute with one variant we have and takes high performance uh AI for both training and inference headon uh with with a variant which is optimized for those AI applications so it's been a long journey and we're really pleased to be where we are where our our sales are taking off no it's fantastic I mean I I guess when you launched the mi30 um you had public commitments from meta and Microsoft for example to purchase that and you just mentioned that there's a series of applications that you're pretty excited about there can you tell us more about which a applications and workloads you're most excited about or Mo most bullish on today sure so if you think about where the bulk of AI is today you're still seeing just tremendous Capital expenditures and building up the accuracy of capabilities for large language model training and inference so it is the the likes of chat GPT of Bard and and you know and the other uh you know llms that you can uh ask at anything because it's trying to ingest the V of data that that is out there and that can be trained upon and it's it's with really an you know an ultimate goal of artificial general intelligence and AGI type of uh of capability and so uh that is where we focus the Mi 300 is to start with that that Halo product that could take on the industry leader and in fact Mi 300's done that it's competitive on training and it leads in inferencing it has over 2X if you look at uh you know fp16 VMS which is a a metric that generally everyone uh can run that it's got a tremendous performance advantage and and we did that very purposely we created very efficient uh engines for the the math processing that you need for that uh training or inference uh processing but we also brought the memory that you need to have more efficient Computing so that's more Computing at less power less rack space uh than you need with competition a big front of competition is as you just pointed out there's performance like overall performance there's efficiency and then there's um like the software platform like Cuda rockem Etc how do you think about the investment in the optimized math libraries and like how you want developers to understand your approach versus competitors yeah you're you're so right Sarah it's multifaceted to be able to compete in this Arena uh you see many startups going after the space but the the fact is the the bulk of entrancing done today is done on general purpose CPUs not the huge llm inferencing but you know just general uh inferencing for AI applications and then for large language model applications it's almost all on gpus because that is the software and developer ecosystems out there and so we've been competitive on on uh CPUs we've been gaining uh share at a rapid clip because we've got you know a very strong CPU uh generation after generation that we've been releasing on on schedules we've laid out for the industry but for GPU it did take us uh until now to develop really worldclass hardware and worldclass software and what we've done uh is ensured that because we're a GPU it it should be easy to deploy uh and so really making sure uh that we Leverage that we have all the GPU semantics so if you're you're a coder uh it's it's just easy to code if if you're using the the lower level semantics uh but also uh we support all of the so Key software libraries that are out there when you think about the kind of Frameworks whether it be pytorch or a founding member of pytorch foundation whether it be Onyx um whether it be tensor flow we are out there very closely working with developers and so what we've now now got to now that we have uh you know competitive and Leadership offering uh is what you'll see is that uh when you're deploying with a AMD very fasil if you're uh let's say you're using hugging face any of the you know thousands and thousands of llms Open Source LMS out there on hugging face well we partnered with Clem and's team they they test as they release any of those language models uh they're testing on AMD with our Instinct gpus equally as they're testing on Nvidia so we've uh really uh done the same thing as well with pytorch where we're one of two qualified uh offerings on uh on pytorch and so all of that testing is being done uh you know routinely with the regression testing that's run literally every night on any software release uh the other thing that's that's key uh is to learn from deployments and so we've had early engagements like lamini who who's running on AMD and they've been they've been offering uh you know um Services of getting on AMD and running your llms on their on their Cloud on their uh their rack configurations they have uh and so they've already been working with customers and now as you saw other people on stage with us at our December event you can see uh that we're in there with a key hyperscaler uh and we're also uh being sold through uh many OEM applications and we're directly working within customers so there's nothing like that feedback from Key customers that are running on your platform uh to speed us uh you know ensuring that we can just be easily deployed and and make sure that that it uh it's a seamless process yeah yeah lamini uh is a portfolio company for me and Sharon and Greg are great I think it's an indication of you guys having a big ecosystem of software developers and machine learning people that want to see uh competition and more heterogeneous compute out there for these AI applications Sarah you cannot underestimate that it tells you that it was a very uh a constrained environment there was there was a lack of a competition which bad for everybody by the way if there's a if there's not competition because you you really end up with a stagnant industry uh you can look at the CPU industry before we brought to competitive and Leadership it was really getting stagnant you're just getting incremental improvements and so the industry knows that and we've had tremendous uh pull and partnership and we're very appreciative of that uh and and in return we're going to we're going to keep providing generation after generation of of competitive product out for such a huge like software stack like Rockham to be open source like talk about that philosophy no it's a it's a great question it's very near and dear to us because uh we are uh as I mentioned all about collaboration that's you know just such a strong part of our culture and what open source does is it opens up technology to the community and so if you look at the the history of of AMD it's been um very focused on open source our our compiler for our CPUs is llvm it's it's open source the llvm is underneath our uh our compilers on our on our GPU but more than just the compiler on the GPU we've opened up the Rock and stack it is it is our enabling stack uh it was a huge piece uh in our uh winning uh supercomputing uh with such large installations we have why is it our philosophy and by the way xlinks had exactly the same uh philosophy and so bringing xyl links and AMD together in uh in 2022 uh didn't did nothing more than um even deepen that commitment to open source but Sarah that the point is we're not about locking in someone uh with a proprietary wall Garden software stack uh what we want want is uh we want to win with the best solution and we want our we're committed to open source uh and we're committed to giving our customers's choice uh we expect to win having the best solution uh but we're we're not going to lock our customers in we're going to we're going to win on Merit uh generation in and generation out I guess one of the areas that I think is evolving very rapidly right now is sort of the clouds for AI compute and so there's obviously the hyperscalers the Azure from Microsoft and AWS from Amazon and GC from Google but there's also other players that have been emerging um you know base 10 together modal uh replicate etc etc and one could argue that um they both are providing differentiated services in terms of different tooling API endpoints Etc that the hyperscalers don't currently have um but also that in part they have um access to GPU and there's a GPU shortage and so that's also driving part of the utilization how do you think about that market as it evolves over the next 3 four years and perhaps you know GPU becomes a bit more accessible and maybe shortages or constraints Fall Away well that's definitely happening I mean that the supply constraint that will go away will be a part of that we're uh ramping up and and shipping as we speak on our Instinct line uh and it's going quite well it's going according to plan but moreover uh to answer your question I think the way to think about it is that it's just breathtaking how the Market's expanding so rapidly I said earlier that most of the applications today that that started on the you know the generative AI with these llms that's been largely cloud-based and not just cloud-based but hyperscaler based because it's such a massive cluster that's required not just for the training but frankly uh for a quite a bit of the the that type of generative AI llm inferencing also is on these massive clusters but what's happening now is we're getting application after application that that is just taking off nonl linearly it's uh and what we're seeing is a proliferation is people are understanding uh how they can tailor their models how they can fine-tune it uh how they can have smaller Noles that don't have to answer uh any question you have or any application you need to support but it might be just for your business and your area of of expl exploration and so that allows a tremendous variety of the size of compute and and how you need to configure that CL so a rapidly expanding Market application specific configurations you need for your compute cluster and it moving even further not just from these massive High hyperscalers to uh you know I'll call it you know kind of tier 2 kind of data centers but it just keeps on going because when you think about uh applications which are really bespoke and they can be run on the edge right on your factory floor where you know very low latency put the put the uh inferencing uh and uh you know right at the source of data creation right to end user devices so we've added U our AI inference accelerators right onto our PCS we we have been shipping it throughout all of 2023 and actually at CES this year announced already uh our our next generation of uh AI accelerated PCS uh and then of course with our zyink portfolio across uh embedded devices we're getting a lot of pull from industry uh that has bespoked infering sample appliation right in a pleora of embedded applications so with that Trend um we we're going to see more of that more tailored uh compute installations uh with with the you know an attempt to service this ballooning demand yeah that makes a lot of sense I mean I guess a lot or a subset of um inference is going to push to the edge and obviously we'll have things on device but on laptops as well as phones in terms of you know where certain small models will be running and then it seems like there may be some Ono do potential set of constraints for larger models or larger data centers at least in the short run um what are the main drivers of the constraints on the GPU supply side is that you know I've heard things around packaging I've heard things around tsmc capacity I've heard sort of a mix of like potential drivers of constraints some people say the next constraint after that is do you have enough power into Data Centers to actually run these I just don't know what's real in terms of all this stuff and I'm a little bit curious like how to think about you know what are the constraints and how do we think about when those those um the supply things come a bit more into balance yeah Supply demand is uh frankly something that uh any chip manufacturer uh you know has to has to manage you have to secure your supply you look uh during the pandemic uh we had uh actually a a tremendous uh run on our devices that that uh stretched our supply chain because the demand for PCs went way up people were working from home uh the demand for our XA six servers went way up and so we were in a scramble mode during the pandemic uh and we did very well we worked uh we we had shortages of substrates and we we secured more substrate manufacturing capability we worked closely uh with our primary um wer Foundry supplier tsmc uh we're we're have such a deep partnership with them we've had it for decades uh that if we get out ahead of it and we understand the signals uh we we gener generally able to uh to meet the supply or if there's a if there's a shortage it's generally well contained uh and so what's happening with AI is uh yes it is clear that we're seeing this uh you know this massive increase in the demand and uh the Fabs are responding and you're having to not think of it just as a wafer Fab but you're absolutely right it is the packaging uh our cells and our GPU competitor both use Advanced packaging I mean I'll show you I don't know if in the camera if it'll come come across here but that is our ni30 and what you see is a whole set of chiplets uh so smaller chips with either you know a CPU function IO and memory controller can be it can be the CPU for what the version we have uh that focuses on high performance compute we literally drop a uh our CPU chip it's right in that same integration and all the high bandwidth memory that you have around it uh to be able to feed those engines and those are connected laterally and on the mi30 we connect them those devices vertically as well so it's a complex supply chain uh but it's one of which we are very very good at we're a fabulous company we've been fabulous for you know coming on 18 years now uh and so we've got it down I hats off to the AMD supply chain team uh I and I think overall as the industry you'll you'll hear that generally we're going to move Beyond those type of Supply constraints now you mentioned power this is I think uh ultimately going to be certainly a uh a key constraint uh and you see uh you know all the major operators looking for sources of power and for us as a as a a developer of the engines which are consuming that power it we brings tremendous Focus uh for Energy Efficiency and that we can drive into uh each generation of our design and we are committed to uh to that certainly at very top priority one thing you said before Mark is that you were actually excited about the innovation of the end of Moore's Law um and that being a reason that you actually wanted to go to AMD like what directions of innovation should we expect investment in I don't I don't know if it's like too deep to ask you to give us a lay man's understanding of like 3D stacking but I I think it is really interesting to to think about it at a at a time when it's not obvious where to go well no sah it's a it's a great question and and the reason that I was so attracted to uh to AMD is one it's it had a storyed history of being a disruptor in the industry uh and and I certainly felt very strongly that AMD could disrupt uh with very strong CPU and GPU but more importantly uh putting the pieces together uh the the idea of chiplets was just coming together there was there was early expiration of that of of that around that uh around that time and uh the engineering uh Team here at AMD were were able to um you know really uh get the team rallied and the the the the key leadership rallied around it and drove that uh that that Innovation so that the the reason it's so important is when Moore's Law slows down you know the easy way to think about it is it used to be that the chip technology itself The Foundry going from one generation to the next did most of the heavy lifting so you could just Bank on that new semiconductor te technology node shrinking your devices giving you more performance it have less power and it be at the same cost so that was what Mo's law was about and with mors law slowing it it means you still get those device improvements but it costs more uh your power is not coming down as much as it used to uh and uh you are are still getting that integration you're still certainly being able to pack uh more devices and but it it demands more Innovation it demands what I call Holistic design so you're you're going to you're going to rely on those new transition devices new Foundry nodes but how you use heterogeneous Computing meaning bringing the right compute engine for the right application a CPU a GPU a dedicated engine like we have super low power AI acceleration uh that we have in our in our PC devices and our embedded devices so it's about getting uh you know tailored engines for the for the right application leveraging chiplets that you combine B them put them on what is the best technology node you want each of those chiplets each of those functions to be on and then frankly holistic design means you got to keep going right up uh through the packaging how you package it together how you interconnect it and how you think about the software stack and so it's literally got it the the the optimization has to be the full circle of transistor design all the way up through the integration of your Computing devices and equally with the view of the software stack and applications uh and what I'm thrilled about along with all the engineers that I work with at AMD is that we we have that opportunity we have the building blocks and we are built on collaboration it's just such a part of our culture uh that uh we don't need to develop the entire system we don't need to be the ones developing the application stack and the end applications what we we do is partner incredibly deeply uh and ensure that the solution is optimized into to end I think everybody is very suddenly interested in the chip industry from a strategic perspective as well I think everybody's thinking more about the supply chain um from the you know tsmc near Monopoly to the idea of Fab Security in an increasingly complex geopolitical environment how does AMD prep for this or think about these issues well you know you you have to think about these things we are very supportive of working with certainly the US governments and and other governments uh across the world which uh have exactly that question how you know our our country is running now on ship design uh that that uh Power such essential systems that uh it becomes a matter of National Security to make sure that there will be continuity of supply and so we build that into our strategy uh we build it in uh with our partners and so we've been supportive of a Fab expansion so you see tsmc uh building Fabs in in Arizona we're partnering with them you see uh Samsung uh building Fabs in in Texas but it's not just in the US they're actually explaning uh as well just a global uh facilities in in Europe and and other parts of Asia and so uh it goes beyond The Foundry it's the same thing with the packaging so where do you as you put those chips onto carriers and you need to interconnect it you need that ecosystem uh to have Geographic diversity as well so the way we think about it is it it is a a matter of importance for everybody to know that uh that there will be uh Geographic diversity and we are heavily engaged and actually I'm I'm quite pleased with the the progress that we're making it takes it it doesn't happen overnight that's the difference between chip design versus software someone can up you know with software you can come come up with a new idea and get that product out very very quickly get that uh you know MVP design get it out there and and it can go viral uh but it does take years of prep uh in expanding the supply chain SE the whole semiconductor industry was built up as historically as well this is a global industry and we'll create Geographic pockets of expertise so that's how we got to where we are today uh but when you have uh you know more volatile uh you macro that uh that we're facing today uh with political tensions with uh you know economic tensions uh it's just imperative uh that we that we spread out uh that manufacturing capability and it's well underway I guess one of the um other things that's been happening a lot recently uh is and you know you've been involved with I think some of the most interesting and exciting new consumer Hardware platforms like iPhone and iPad and other things and obviously um AMD now is powering many uh interesting types of devices and applications um what's your point of view on the new hardware things that people are building today there's the Vision Pro there's rabbit which is sort of an AI first device there's Humane focused on the health side there's figure it seems like there's suddenly an explosion of new sort of Hardware devices and I was just curious to get your perspective on what do you think tends to predict success for those types of products um what tends to predict failure like how to think about this whole sort of Suite of Suite of new things and devices that are coming our way well that's a great question I'll give you um you know one point I'll start just with sort of a technological point of view I mean uh I'm proud of the fact uh that uh chip design uh is part of the reason you're seeing all these different type of applications because you're getting more and more compute capability that is shrunk down and and draws such a low power that you can you can see uh more and more of these devices that have simply incredible uh Computing and audiovisual cap capabilities uh that that they can bring to you I mean you look at meta Quest and Vision Pro and things like that this isn't happening overnight it's it you look at the earlier versions they were simply too heavy too big not enough Computing uh ump because if the uh the lag between you know seeing a photon on the that screen and on your head-mounted device and actually being out of process if that lags too high you actually get physically ill wearing uh you know wearing that and trying to watch a movie or play play a game so one I'm very proud of the the technology advances uh that uh we've been able to make as an industry and we we're certainly very proud of our aspects that uh that we drive from AMD uh but the broader question that you've asked is well how do you know what's going to be successful the technology is enabler but uh if there's one thing I learned at Apple uh uh the devices are successful really serving need I mean they really give you a capability that you love it's not just that oh it's incremental I can do this a little better than something else I did before it's got to be something that you love and that creates a new category uh so it it's enabled by technology but it is the product itself that has to really excite you and give you new capabilities I will mention one thing I mentioned the AI enablement and PCs uh that's going to I think it's almost going to make PCS a new category because when you think of the kind of applications that you're going to be able to run uh with with super high performance but yet low power inferencing you can run imagine right now if I'm I don't speak English at all and I'm watching this uh podcast let's say it was a l you know if it's broadcast live and I click my live translation button I I could just have it translated uh with to my spoken language with no perceptible delay uh and that's just one of a myriad of new applications uh that that will be enabled yeah I I think it's a really interesting time because for many years like increasingly and am day benefited from some of this right you're also in um in the data center but there was so much compute load moving to uh servers right ARA of cloud era of like all these like you know complex consumer social applications I I think in like in the new era of trying to create experiences and fighting like all these like new application companies are fighting latency as a a primary consideration because you have you have the network the models are slow you're trying to chain models and you have you know things you you want to do on device once again um and I just think that hasn't been like a real design consideration for a while sir I I agree with you and I think it's it's one of the next set of challenges uh and that is really tackling the idea of not just enabling a high performance and AI applications on the cloud on the edge and these end end user devices but thinking about how are they working together synergistically writing applications that where you don't have that latency that uh you know that dependency on on a lag in Computing run it on the cloud it's going to be the most uh it's going to be the most efficient because you're optimizing this massive data center uh with the most efficient computing but write the algorithm such that where you do have that need for super low latency you just need that instance response have those aspects of the algorithms be at the edge or in fact uh on your end user device and often when you need to react quickly uh it just has to be the case I mean uh do do you want to to be in your ve vehicle which is being driven uh at a high degree of autonomous driving suddenly get a a loss of signal back to the cloud and and you just stop you know because it says I don't have a signal you you wouldn't stand for that so our our audiences lots of uh Engineers Founders Tech Executives um consumers too what what do you want people to know about that amd's focused on in 2024 well this uh for us is a is a huge year because we uh has spent so many years developing our hardware and software capabilities for AI we've just completed uh AI enabling our entire portfolio so Cloud Edge uh you know our PCS our our embedded devices our gaming devices we're we're enabling our our gaming devices to to upscale using uh AI uh and 2024 uh is really a huge deployment year for us so now now is the the bedrocks there the capabilities there uh I talked to you about all the partners that we're working with uh so 2024 uh is is for for us a huge deployment I think we're often unknown uh in in the AI space everyone knows our competitor U but we not only want to be known in the AI space but based on the results based on the capabilities and the value we provide we want to be known uh you know over the course of 2024 is the company that really enabled and brought AI across those breadth of applications yes in the cloud and those you know massive llm uh training and inference uh for generative AI but equally across the entire compute space and I think this is also the year that that expanded uh portfolio of applications comes to life uh I look at what uh Microsoft is talking about in terms of the uh enablement that they're doing of capabilities uh Cloud uh to client uh and uh it's incredibly exciting and and many many is feeds that I've talked to are doing the same thing and frankly Sarah they're addressing the very question you asked how do I write my application such that I give you the best experience tapping both the cloud and the device that's in your hand or in you know in your your laptop uh you know as as you're as you're running the application uh so it will be a transfor transformational year and we're so excited at AMD uh to be right in the middle of it uh awesome looking forward to the year ahead and seeing great things thank you so much for joining us yeah thanks for joining us well thank you both this is like I said you guys have just done a a wonderful job here with no priors and uh very um happy and uh appreciative that you invited us on and L to time with you it's real pleasure find us on Twitter at no prior pod subscribe to our YouTube channel if you want to see our faces follow the show on Apple podcast Spotify or wherever you listen that way you get a new episode every week and sign up for emails or find transcripts for every episode at no- pri.com