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Hello everyone.
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Welcome back to Semi analysis. My name is Jordan. I'm here with Abalash.
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This week we are going to do something a little bit different.
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We're not talking about an article.
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Instead, we're giving everybody a little bit of an explanation on the semi- analysis consulting business.
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Avalash leads our consulting business. He's got a great team.
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And uh a lot of people have some questions about like what we do in consulting, what types of clients we serve, what are the projects that we do and things that we've learned.
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Um this covers everything from like DDS for investors or companies that are looking at M&A act activity to investors who want to go beyond the core products and you know customize something about one of our research products like the accelerator model, data center model, things like that as well as uh strategy guidance you know feedback on product road mapaps go to market activities for lots of clients and industries.
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So Avalash welcome to the show excited to have you today. >> Thank you Jordan.
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uh excited to finally make my debut uh to the renowned semi analysis weekly.
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>> Avalanche and his team have lots of opinions that it get expressed on this podcast, but for the first time we're going to hear it from him directly.
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So, I'm excited for that. >> Oh, absolutely.
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>> Okay, give us a quick overview.
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What's semi analysis consulting business? What do we do?
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>> Uh I think so, so by the way, Semi analysis has been doing consulting ever since I think Dylan started semi analysis.
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But it was only last year I would say when we set up a dedicated team and the reason is semi analysis does a lot of things.
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We we publish our newsletter.
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We have institutional models.
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I think we have close to 13 to 14 different institutional models right now.
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But a lot of this and we have open projects open research projects like infinsex and cluster max.
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So there's a lot happening but a lot of this stuff is actually very offtheshelf.
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So if any industry participant or any investor wants to engage more deeply with semi- analysis, we didn't really have an operating model internally to be able to service those requests.
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So that's that's the genesis of semi- analysis consulting.
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I think what we do is we work with various different clients on a very dedicated basis.
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All the work that we do is very custom.
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Uh we typically staff a dedicated team uh for anywhere between 3 to 4 weeks to all the way from a year.
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And uh I'm happy to talk more about it, but uh that's essentially what we do.
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All the work that we do is custom. >> Yeah. Awesome. Custom work.
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Uh maybe start by talking about the types of clients that we serve.
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I gave a little higher level overview of like the uh um different types of projects that we've done.
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But uh yeah, take us through that. >> Yeah.
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Uh in my opinion, I think what we're witnessing is something called like we always talk about hardware software core design, right?
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design, right? I think in the world of uh AI infra there's something else that's also happening which is which I call tech finance code design I would say so we need to teach uh tech bros more finance we need to teach like more finance bro finance bros more more tech and that's essentially what we do uh I think there are two industry there are two sorts of client groups that we serve the first one is the financial
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institutional investors and these are large private equity funds these are large hedge funds uh who are very uh savvy investors and they are very excited about investing in AI infra and now when they when they're deciding to drop a billion dollars $2 billion in a chip company in a neocloud in a data center they want semi analysis to come in and essentially help them figure out various aspects of the deal. So we do
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So we do various sorts of diligences for them.
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Uh we also do a lot of thesis development for hedge funds.
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A lot of hedge funds are looking for the next wave.
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You know, people got excited about memory, people got excited about CPU.
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So, they're trying to think about, okay, what's next?
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And we also work with a lot of hedge funds in combining various different data sources and different market narrative to figure out what's the next wave.
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the next wave. uh on the other hand uh we also serve a lot of industry clients and these are I would say largely the hyperscalers the AI labs the chip companies uh semis companies and here the range of topics can can be really really large uh it could range
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from helping a semi company figuring out their product and engineering road mapap so a common question is if you know LLMs are evolving in a certain way what should be the products that we should be thinking about 3 years out, four years out and 5 years out. Uh it could also be
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Uh it could also be helping certain hyperscaler figure out data center sites in remote remote locations in India for example.
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Uh it could also be doing go to market strategy for various different companies who have a strong product but don't know how to connect and figure out their customer journeys.
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So, it's a range of different options, but that's essentially the type of clients that we serve as part of semi analysis consulting. >> Makes sense.
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And quick advertisement for Avalash on his behalf.
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If you're interested, if you're listening and you're interested in tech, finance, code design, uh you should consider applying to work on consulting as semi analysis. It's a lot of fun.
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>> Um >> we are we are hiring a lot.
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So, if shameless plug, if you're a AGI build, excited about semis, curious to learn, please hit us up.
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We are desperately in need of people. >> Yeah.
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So, take me through some of these customization examples.
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Like, um, the place where I get involved in some of this work is that we're taking research that me and other guys on the team are doing and trying to package it up in a more digestible way as well as customize it for somebody's specific interests.
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So, take me through what that process looks like where we lean on our existing data sources to actually go and execute on these projects. >> Right.
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>> Right. I think uh because semi- analys at semi analysis we have so many different things that are happening usually if you're able to just combine some of these data products in a more comprehensible manner we are able to answer some of the questions but that
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each of these data products are not able to answer by the by itself so I think one of the examples was u very recently uh there was a hedge fund client that was very interested in understanding what's the total token supply in the world assuming the chip to install base
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and and if you think about it right like it's a fairly simple equation what is the total installed base in the world today and what's how how is it expected to grow in the next 5 years how much of that capacity is allocated to training versus inference if you have the install
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base the the second part of the equation is what is the throughput per GPU and how much how many tokens can each GPU uh generate uh in a certain day in a given year and finally what's the pricing uh in terms of dollar per million tokens for these tokens. Each of these three each of these three
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Each of these three each of these three different parts of the equation got answered in a different manner but the most complex was estimating the throughput per GPU because we had to combine a lot of our inference X data that's already public but on chips that are not out yet and on models that are not out yet.
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yet. So we had to build an inference simulator where we had to estimate okay if Nvidia comes up with framemen with certain set of characteristics what does it mean for the throughput if memory gets despec like it has happened what's the impact on throughput if openAI trains a 10 trillion 15 trillion 20
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trillion model like parameter model what does it mean for throughut now a lot of this is not readily available able uh and something that we had to build out custom and we had to work with you know various inference engineers at semi analysis to be able to do that but that's an example of a project that would be custom work. >> Yeah. Okay. Take me through some of >> Yeah. Okay.
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Take me through some of those assumptions there like in the inference simulator. I know.
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Um well maybe explain how it gets delivered to the clients because I I think a lot of people hear consulting and they think slide deck >> and we make plenty of slide decks but for complicated questions like this the trend for us has been to actually move towards live dashboards where people can edit their assumptions over time and we have to maintain this.
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So it's kind of like a software product that we're delivering to them. Right. >> Exactly. Okay.
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So, so this is like like there were so many permutations and combinations in this product that it was not feasible for us to put this in a static Excel.
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So, we had to build a dynamic dashboard and that was essentially what was delivered to the clients.
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This is a dashboard that they could use play around with different sets of assumptions in a very visual manner and see literally every number changing.
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changing. So the key assumptions here were I think to start with how much uh how much of your compute are you allocating towards inference versus uh training um what are the like we know the existing chip specifications but
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like what are the chip specifications that you're imagining for the next set of silicon that are being developed by AMD TPUs Nvidia >> we know from one of the episodes two or three episodes ago that those specs can change pretty quickly. Ruben Ultra just
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Ruben Ultra just got despbacked from a terabyte of HBM down to >> 56 or 128 or 192. So >> yeah. >> Yeah.
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>> What's your like another set of assumption could be like around what's your input output ratio?
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Are you using agent X harnesses?
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Are you using like fixed sequence length uh uh context length uh uh like sort of data types?
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What's your cachet ratio?
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I like how do how does throughput change if you're hitting like 80% cache versus 85 versus 90%.
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So I think those are some of the assumptions that make it uh interesting.
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>> Yeah, makes perfect sense.
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Now um tell me more about an example that goes for a different like category which is uh like strategy.
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Um, you know, this could be on product roadmap.
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It could be on like a business decision about build versus buy versus partner.
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What what are some of the domains that you're helping people with right now? >> Yeah.
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Um, I think strategy is a very broad term I guess.
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Uh, and it could it could mean like product strategy, it could mean like go to market strategy, but I can give you an example of both.
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Uh so as I said like one of the things that we have built internally is an infant simulator and and like one of the you know memory storage companies is essentially using that simulator to replay like egentic traces uh for a combination of different models uh um as well as chips to figure out the next set of uh memory products that they should be developing.
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uh and they're you know answering the questions like you know how does memory bandwidth versus uh like sort of capacity trade-offs play out in in serving production inference workloads.
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So that's one examples of like you know a product strategy thing that we're doing and we're like working with their teams on a very dedicated basis.
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The other example could be we're helping um you know one of the semis companies figure out their ideal set of customers.
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So they have a product but they don't know who to who to sell to like there are probably like 100 customers that that that could buy that product uh at any given point of time but it's it's a long lead time product.
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It it takes at least 3 to 4 months to qualify a customer.
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So it's not realistically possible to go and qualify your product with like 100 different customers, right?
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Uh so we are helping them figure out who are the top 10 or 15 customers that they should be prioritizing.
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what's the right way for them to approach like sales conversations?
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What are the key technical topics that they as customers would be interested in and like how should you position yourself to be able to like deliver on those technical specifications that these customers want.
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It seems easy to say it's very difficult uh uh I think to execute because we need to go out and literally speak with 20 30 40 different customers get their perspectives aggregate a market view.
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uh and this is something that a lot of different companies are not able to do because semi analysis is so very well connected.
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We don't really use an expert agency to carry out these expert calls or whatever you call them.
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Uh because we have such a wide network, we are able to just use our relationships to figure out important questions that would help the wider ecosystem. >> Yeah.
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And this also informs our research.
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research. I mean um maybe you can explain a little bit about how uh people who have been on this podcast in the past like say Eric a few weeks ago working on modular data centers are uh you know when they go and do this research effort it contributes to some analysis products and in many ways some
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of the um institutional products that we sell the data products originally started as a consulting project or at least the inspiration to start working on that actually started because somebody's asking us about this they clearly think it's important important therefore let's
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go do it right >> right uh because I think because we work on a very custom basis with a lot of these clients and we go very deep on a topic I think often times we're able to identify that there is a like if this question or problem is relevant for one client and we have been able to figure
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out a standardized solution for that I think we're able to like scale that up to a broader set of clients and that becomes a product I think a data center model started as a consulting project for one of the clients maybe cluster max started as a consulting project. I don't I don't know.
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know. Uh you have to tell me that no >> cost max but the the AI cloud TCO model definitely started with the consulting projects which was you know we we continue to use to build analysis on like >> how uh you know what are the input costs
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for GPU clusters how profitable are given companies that are serving tokens based on the choice of chips based on the amount of opex based on the as you're saying utilization ratio on a inference endpoint or something like that. The way we we actually dig in and
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The way we we actually dig in and do that is like >> you get inspired to do the work because people that you trust that your your clients are taking some problem seriously and asking questions and you know let's go get answers that that becomes a useful product.
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So anyway, >> exactly and I I think the other the the latest example of that is the inference simulator that we're building, right?
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I think we started building that for one client and and not even for one client.
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I think it started as a cool like science project within semi analysis and then we offered it to one client, they found value in it and then a few other clients found value in it and now it's becoming a consult like sort of a product by itself. Uh uh so absolutely.
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>> Yeah, makes perfect sense. So how about uh DDS?
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That's obviously a big thing for the investors as well as some companies that are interested in M&A activity.
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>> I think u you know Dan talks about $7 trillion of debt being issued to fund AI infra right.
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Uh so there's a lot of uh I think investors that are coming into the game.
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Uh there are a lot of newer players that are also coming into the game.
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game. And so what a DD project essentially looks like is an investor coming to us and asking us hey can you either help us if let's let's take a neocloud for an example right because that's what we do a lot of the times uh if if a private equity fund wants to put
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in a billion dollars in a neocloud essentially they're looking for two things they're looking for a technical analysis and they're looking for a commercial analysis on the technical side I think what they're interested in is what are the SLAs that a customer
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that the NeoCloud is offering to the customer and will this neocloud be able to deliver on those SLA guarantees because if they don't there are like wider set of implications that we're going to talk about later but it's not good for any of the parties right um
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they're also interested in understanding what is the OEM support and warranty structure and contracting looks like because there's a lot of devil in the details there and if you're not transacting properly with the OEMs you could you could expose yourselves to a variety of risks. They are also
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They are also interested in understanding the cluster performance.
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So we are able to test their clusters with a very uh cluster max uh inspired methodology and be able to tell what exactly are the things they're good at and what exactly are the things that really really bad at and what's the 180day plan for them to be able to improve on those things.
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able to improve on those things. I think on the commercial side because it's such a financially savvy decision they need to understand what's the capex and the opex uh for these builds and how does it compare really to the market standard because you know as part of our TCU model we are able to benchmark in real
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time costs uh as well as you know like through various other means like performance uh we are able to uh benchmark these things in real time for these clients so that's what like a typical DD projects looks like and Uh it it it it varies depending if you're diligencing a neocloud versus a chip company versus a model company. We've
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We've done or a data center or a or powered land and we have literally done everything.
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Uh I think we've done a lot in just uh just a year or so since you've been here.
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And I mean we're just like stacking up experience across it, building the team.
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And it's pretty cool to see it kind of grow as uh as people really as as more and more people in the industry take this stuff seriously because I I think uh the need for technical due diligence on a lot of these projects is of the utmost importance.
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It it's all of it's new, right?
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It's not like um certain investors can understand or have the capability to understand, you know, a type of model that's 6 months old or 3 months old.
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months old. I mean it's new for everybody right um anyway >> exactly it it's new for everybody and also I think what's happening is that in my opinion I think the offtaker base is diversifying beyond the top labs to also like now the neolabs who are actively transacting in the market the neo cloud
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operator base is also diversifying if you think about it right like hyperskllers are the ogs and then you have the crypto miners and then uh specialist players like you know core and like others but And now you see a lot of real estate developers who have who do not have any experience in operating cloud entering the scene. You
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You see family offices of rich people trying to build a new cloud now.
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And so SLAs's become even more important because if you don't have the experience of operating such things before and if SLAs's are not in your favor like the implications would be really massive. >> Yeah.
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Let's talk about the two types of them.
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Obviously, this is something I'm quite familiar with.
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There's one uh type of, you know, SLA that's related to the delivery timelines.
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Really, an SLA is just like if something happens, a contract can be terminated.
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That's roughly the the legal ease behind uh a service level agreement.
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And it doesn't it you know, contract termination rights is maybe a more broad definition and that's really what we're talking about here as opposed to SLAs.
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First reason why you might be able to terminate a contract is because of late delivery.
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Cluster shows up months late.
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So therefore cancel the contract.
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But the most common one is downtime.
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If uh somebody's got GPUs installed, they expect to have 3 9 2 9 of availability.
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And if you're under 90%, you know, one out of 10 days in the month, three out of 30, you don't have access to the GPUs you're paying for.
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generally you're getting some credits in response or you have the right to terminate the contract.
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So maybe talk to me about the motivation for the lenders or for the um Neocloud itself or even for the buyer uh to have some sort of insurance against either of these possibilities of a of a contract termination.
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They want downside risk protection. Right. >> Exactly.
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I I think so so Jordan you hit the nail uh right on head right there.
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Uh so one why SLAs are important and then I'll talk about why insurance is important.
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I think you said it right.
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The first thing is as a neoccloud without strong SLAs's you won't be funded like period like you won't be able to start your journey like all the investors all the lenders the most important thing that they care about is what is their risk exposure
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if they're getting interest payments every month every quarter what is the chance of that payment not hitting their bank account because your cluster is down so that's the number one thing I think the second thing is uh I would Hey, SLAs are really for the bad times. Uh,
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Uh, you know, we have seen this in various like settings.
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If things are going well, nobody talks about the SLAs's.
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If things like, you know, don't go well, people talk about the SLAs's and it gives sort of the the parties to to be able to get out of the contract uh scot-free.
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Uh, and the third thing is, you know, we have talked about it at least for Neo clouds, uh, they typically sign a 15 20 year data center lease.
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they typically sign the GPU contracts for five years.
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And so if you're not committing or if you're not sort of uh delivering on your SLAs, you carry a reputation risk which means that you will not be able to get your second deal and you you'll not be able to get your third lease, right?
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And if you're not able to do that, then how do you like pay for 20 years of data center leases?
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Uh so SLAs are important.
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I think reason why it's important for lenders as I said uh is they're looking for downside protection and one way to protect your downside is by having an SLA insurance.
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We know a few companies now who are uh ensuring SLA uptime uh for the NeoCloud.
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So if as a NeoCloud if you pay a certain basis points of premium you know you get a certain protection that if you have to pay an X amount of penalty the insurance company will take care of it.
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It's not easy in my opinion to get this insurance because these insurance companies are also pretty savvy in terms of how they underwrite these neoclouds.
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So it's not an excuse to have bad performance that you know you can continue to have bad performance but then you will not and and then you'll kind of compensate for it by having the insurance.
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If you have bad performance you won't even get insurance.
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But if you have good performance, I think a way to protect your downside and make maybe your contract more investable is to probably think about electing for some of these insuranceances. >> Yeah.
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Now, um we've thrown around the term SLA and and uh talked about a few different things.
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Obviously, there's many different components of a given uh cluster or data center or site that can have an SLA attached.
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So this could apply to the GPUs or the nodes themselves.
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It could apply to the racks.
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It could apply to the clusters.
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It could apply to the site itself.
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It could also apply to the power systems and cooling systems in there.
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And when there's so many parties at play doing this and there's so many contracts trading hands, there's actually a lot of reasons why you might want to have a risk assessment done for just even one individual part that you're less comfortable with when compared to, you know, some other part of the contract that you have more confidence in because maybe you've worked with that vendor before or because you understand the technology better.
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better. talk to me just about the concept of a risk assessment and the scope because well I know the I'm leading the witness here but the the scope can be pretty tight pretty quick or it can be pretty big pretty broad right
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>> right right >> I think uh as you said like a risk assessment could be like fairly critical especially in cases where lenders are or investors are demanding for it and that's a necessary condition for them to fund a particular sort of a cluster. I
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I think it could be as lightweight as doing a contract review of the of the most important like sort of contracts for a neo cloud.
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And these are the offtaker contract, the OEM contract, the DC contract.
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Uh because you're promising something to the customer and then you're relying on the DC partner and you're relying on the OEM to be able to service that.
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There like other side contracts as well that we can get into but like we don't have to right now. But that's one.
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that's one. I think the second thing is a more like a slightly higher touched review of or risk assessment you can call it which is data center readiness which is understanding the milestones around when will the data data center clear like L3 L4 L5 testing when will
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the data center be made available to you to do like a bunch of different other testings uh when will the racks arrive on the data center uh when can you install like sort of power and cooling and when all of these things can be tested as a system all at once. Uh and
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Uh and we can talk about like other aspects here as well.
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Uh the third thing is doing a bare metal review which Jordan I think you are the master of with all the cluster testing you have been doing.
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So I think I'd like let you do it and then we can come back for the for the final one. >> Yeah.
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Yeah, I mean the summary is basically that we get access to the systems.
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We log in, we test them, we do things like simulate failures and we test to make sure that people have the monitoring system set up so that they can identify that the failures occurred and how quickly they can recover from it.
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There's many, many different ways in which things can fail in a cluster and your monitoring systems have to have full coverage because we've seen things fail in spectacular ways even in just our few weeks of testing um where it's like a fully contrived environment.
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people have have all hands-on to set it up just through cluster max testing and then we hear the stories from all the customers of these providers about how for the quality of services that they're experiencing. >> Yeah. Yeah.
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>> Yeah. Yeah. And the root cause can be like really like sort of varied right and I think this assessment helps you understand what exactly is the root cause of bad performance like is it because problem in sort of like the way your electricity is set up is it because
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of the way your cooling is set up is it because of the challenges in the racks itself uh is it because of the challenges in software and ultimately it's like only one like even if there is only one reason that like your cluster is not performing well it still does not mean that you don't have to pay the SLA penalties. So like only one thing has to
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So like only one thing has to go bad for you to like be liable for paying all the SLA credits back to the customer. >> Yeah.
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I mean that's why we go back to the contract review stage which is the first one, right?
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People need lots of advice on how to set up these contracts because >> yeah, >> as you said, contracts are for when >> there's bad times, not when there's good times. >> Yeah.
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>> Um okay, let me ask a kind of related question, but it can keep us going here.
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when we talk about um giving strategy advice and stuff, a lot of people are getting into this space where they're considering build versus buy specifically with the Neoclouds cuz you know there's such long lead times.
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It's so hard to find a data center site and we've done a little bit of that consulting work where we've helped people make that build versus buy versus partner decision and we've done it in more than just the Neocloud decision for other stuff.
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Let's say supply chain management if you're a chip company or something.
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So maybe you can talk a little bit about those examples as well.
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>> Yeah, I think uh uh because we are definitely seeing a lot of constraints around getting colo capacity to do uh sort of large site buildouts uh for GPU clusters.
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Uh we are also seeing some tightness you know around chips uh and there are like definitely shortages around labor.
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So in principle even when like a lot of the companies want to eventually build their own clusters that that is often times not the most feasible path uh to get near-term capacity and so ultimately it boils down to a few factors I think the first is time to market second is your willingness to pay a premium to get compute uh third thing is how technically sort of savvy your own team is and do you have any experience in running cluster operations?
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Uh and finally in terms of financings and everything like do you have uh enough credibility in the capital markets to be able to raise funding to set up a cluster from scratch or to be able to be an offtaker of a company.
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And so recently we have been working with a few clients where we have let we have we have arrived at a few different uh answers.
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uh I think in one of the cases you have recently advised a company to be a to just like go out and rent because they wanted 100 megawatts in literally in the next six months and there was no way they were able to they would they would have been able to build a cluster that fast.
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Uh but there have been cases where somebody was willing to wait until 2028 and 2029 and we and and the recommendation is completely opposite.
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Let's say these are typically um three to four week projects.
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Uh but like definitely very interesting from the range of content we are able to cover uh and options we are able to explore with them.
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>> Okay, personal question.
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What's your most what's the projects you find most interesting right now of this whole suite that we've been talking about?
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And what's your favorite?
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Is that the same thing or is that a different project?
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>> So you asked favorite and what else? >> Most interesting.
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You just said it's an interesting project.
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I assume that means you're learning and you know doing interesting things. >> Yeah. Yeah. Yeah.
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I would say like one of the most interesting projects right now that we're doing is where where we are like there's a bigger company that's looking to acquire a smaller company and uh that company doesn't have a very big team but they have very sharp people and so I get paid to speak
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to very like to a lot of these very sharp people and I get to learn a lot in the process about know deeply technical projects about know LLM architectures kernel engineering like GPU optimizations, fast inference, slow inference, high throughput inference, like whatot. I think any of these topics
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I think any of these topics definitely sound very interesting to me.
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I think my favorite projects are the ones where we are able to just use a repeat repeatable process to to get to an answer because we have done so many of these new cloud diligences projects over the last one year that now it has just become muscle memory like we know
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exactly what information request list we need to send out on day two, what interview we need to conduct on day four, what five follow-up questions that we always anticipate land up on day six, What's the midpoint check like readout that we need to do on day eight? And
30:54
And finally, like what's the conclusion?
30:55
So yeah, it's it because the conclusions are not predetermined.
31:03
You can't be saying that on the podcast, man.
31:08
>> You'll be surprised how often they are.
31:11
How often times they are. Yeah.
31:14
>> The for the Neocloud diligences, you think it's pretty straightforward to analyze some of these contracts and come to pretty clear >> Yeah. Yeah. results right away. Yeah. >> Yeah. >> Yeah.
31:24
Kind of cookie cutter at this point, but it makes sense.
31:25
Makes sense that people want the peace of mind that you know, we've uh reviewed a lot of the stuff. >> Okay.
31:31
How about um I'll you know ask you a more broad question.
31:37
What are you what are you uh what are you most excited about going forward with with consulting?
31:44
>> I would say uh we are definitely a very very fast growing organization.
31:47
I think the breadth of topics that we're covering today versus 6 months ago uh is a lot higher and I think what I'm more more excited about is partnering with like literally like we already partner with the people who are building in the frontier of AI but getting more opportunities to partner with such people I I think it's definitely the most exciting thing because you get to learn so much uh in such little time.
32:12
Uh it's crazy like how fast the knowledge graph and the learning graph here is at semi analysis.
32:20
Uh the other thing I say is uh definitely Dylan has told me to hire 20 more people in the next 6 months.
32:29
I don't know how I'm going to do that but that will be an exciting journey as well like interviewing a bunch of people spending 50% of your day on hiring. >> Yeah. I know.
32:43
>> It sounds like your current day is pretty exciting because if you're excited for that, you're it's just more of the same.
32:50
>> More of the same, man. More of the same. Yeah. >> Cool. Okay.
32:52
Um, what do you think jumps to mind when I'm like, what did we not cover in this call so far?
33:01
>> I think we have pretty much covered everything that I could publicly say to be honest. >> Okay.
33:08
Because a lot of a lot of >> talk about what you can't say publicly. Amalash. >> No, no, no.
33:16
Um, >> there are a lot of things that I can't say publicly, but I can't say them. >> Yeah. Oh, man.
33:24
Well, I appreciate you joining us today. It's a lot of fun.
33:27
Uh, love working with you on a lot of this stuff.
33:31
Uh, hopefully next time we can have some more guys on the team.
33:33
we can give an update on sort of trends that we're seeing as we uh we execute on a lot more of these projects and we keep learning.
33:42
>> Yeah, I think today today was more about I think what consulting is.
33:44
We should definitely do an episode where we talk more about like what what our learnings are because there are definitely a lot of learnings we have as a team and I think the team will be excited to come and just share everything that we have learned so far. >> Yeah.
33:57
Well, I'm sure the audience would be excited for that, too.
33:59
But we'll leave that for a little teaser.
34:00
A teaser behind the the podcast payw wall coming soon.
34:05
>> Uh 15 minutes of podcast behind the payw wall. How about that?
34:07
Everybody loves newsletter because it's free, but there's a payw wall there.
34:10
So, how can we paywall this video? >> Let's do it, man. Let's do it. Let those dollars come.
34:18
>> We might need to watermark right over our faces, too.
34:20
I think that might be a better thing. Yeah. Yeah. >> Yeah. Yeah. like >> uh translation. >> Yeah. >> Cool. Good job today.
34:28
Thanks everybody for listening. See you next time. >> Thank you guys.