Cognex: Vision Quest - [Business Breakdowns, REPLAY]

This episode is brought to you by Portrait. It's the AI research system that I used to prepare for today's episode and for all business breakdowns episodes. Portrait was built by former buy side investors and they understand great investing isn't just about having more information from low quality sources. It's about having the right information organized the right way. And if you listen to the show, you appreciate diligence consists of many things diving into the history of a business, framing the nuanced competitive dynamics, tracking key signposts around your thesis. And historically, that would take up material time that you do not have. But Portrait is basically like adding an army of analysts to your team. It's powered by an AI system specifically designed for investment research workflows. So you get nuanced idea generation. Portrait assesses the same types of qualitative attributes that we discuss on this show. And that can help identify businesses which fit your frameworks. Portrait also customizes research report generation. And I used Portrait to generate a primer and lay out bull bear cases ahead of today's episode to help frame the conversation. And third, there's intelligent thesis monitoring. And that's where Portrait assesses thousands of data points across value chains each day, extracting the insights, driving the business. Again, all this work would typically take hours and hours and hours. It's at your fingertips now. Visit Portrait Research.com to start your free trial today. This is Matt Russell. We have another replay this week before we get back to regularly scheduled programming. And this one is on cognics. And I remember doing this episode with Brett Larsen and leaving thinking to myself, this is the AI and robotics story that I want to monitor. Because cognics has such an interesting position with machine vision, all of the algorithmic tools associated with machine learning. Since this episode has been released, the company has talked in several different events about some of the progress that they've seen using AI to expand their customer base, use new use cases, particularly with physical AI. So it's only one piece of the broader cognics story. Obviously, there's much more that goes into this business with all of the different end markets and who their natural customer base is. But wanted to replay this one given where we are in the market, particularly in 2026 and so much focus on applying AI into the robotic space. I hope you enjoy. This is Business Breakdowns. Business breakdowns is a series of conversations with investors and operators diving deep into a single business. For each business, we explore its history, its business model, its competitive advantages, and what makes it tick. We believe every business has lessons and secrets that investors and operators can learn from. And we are here to bring them to you. To find more episodes of breakdowns, check out Colossus.com. All opinions expressed by hosts and podcast guests are soleater-owned opinions. Hosts, podcast guests, their employers, or affiliates, main maintain positions in the securities discussed in this podcast. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Today, we are breaking down machine vision leader cognics. Our guest today is Brett Larson from NZS Capital and Brett returns to business breakdowns. We covered trained technologies last year and Brett gives us a different angle on the industrial economy with cognics today. We get into the history of machine vision and how cognics is a factory player. But I particularly enjoyed the strategic angles of this discussion. This is not your typical reoccurring revenue story. This is a lot of that with Brett in this discussion. I was very interested in the conversation going as I was doing my research. Easiest place to start is just with an introduction to who cognics is and what they do. Thanks for having me, it was fun. I was excited to be able to join you again. This is a very interesting conversation. I think it's a very interesting conversation. And the application is generally fallen to four categories. So there's guide, gauge, inspect, and ID. So there's a software aspect to them. They have to link into whatever you are working on. In some ways, that software is machine to machine execution. Is there a lot that's also going back to humans or human interaction with the software? Ideally, the only time the humans really interacting with the software primarily would be upfront when you're programming the application. And then after that, the camera is really sitting out on their factory floor, capturing the data, analyzing it. And then that data will be communicated to more like the programmable logic controller that Rockwell might sell, which is then performing whatever action needs to be done. Ideally, once it's out on the factory floor, the humans kind of out of the loop, other than overseeing it. I think you brought up a great visual in terms of an Amazon sorting facility. And how much of that is now automated? You don't have the humans involved moving the big packages here. The smaller packages there. So it's gotten more advanced. But how big of a market is this machine vision world today? And I'd also just be curious how much that has grown recently. If there's been a step change in terms of the growth of the market. There's a lot of different tam estimates out there for machine vision. But cognizance doesn't play in all the different niches within a packet captured usually. Cognizance last estimated their serviceable addressable market at 6.5 billion, which was up from 2.9 billion in 2017. And they'll probably update their SAM again, let their investor day, and it'll probably be something like 8 to 9 billion based on new products categories they've entered. Industries probably grown at a 10% type of keger of the past decade. And obviously there's cycles in there. And then there's dispersion between some that are growing above that range like cognizance and some that have been below. I like the SAM terminology. I might have to mix that in with tam in the future. Where does cognizance rank in terms of market share within the market? What are some of the characteristics about their positioning and then just the market overall? Cognizance is number two behind Keyens, which is a company based in Japan. The two companies do obviously compete, but they've also historically focused in slightly different areas, which is interesting. So cognizance, they historically have focused on the top of the pyramid when you think about the sophistication of the customer. Typically they're hiring very trained engineers. We're working with customers to spec systems for specific tasks. So it's a more technical sale with very sophisticated customers that are automating very complex tasks. Basically, they're not winning on price. They're a more expensive vendor of machine vision. But they have a reputation for having really good application engineers, the best tech that they can differentiate at the spec level. So for example, that logistics facility where Let's start. you're scanning 100,000 packages a day, cognics usually they can deliver read rates that are 100 basis points or even 200 or 300 basis points better than a peer. So over even just a day, that's thousands of packages that don't have to have a human there to look at something. That's usually how they go about competing and there's a really strong brand reputation as well. So like if a new COO of a company walks into a factory for the first time and sees the yellow cognics cameras, that says something to them. The installed base is very sticky as well for all the vendors and I'll foreshadow a bit but they're focused on the top of the pyramid. Currently, they're also brought in out a little bit lower as well, which is interesting. And then Key Ants, as I mentioned, is number one, they're based in Japan. It's been a really successful company and it's also very much an enigma just in terms of it's a public company but there's just not a lot of intel out there on it. My best guess what I've gathered is maybe 20% or so of their sales are comparable to cognics. They also do things like scientific microscopes and PLCs and drug park marking and stuff like that. But what's also quite interesting is they spend 2% of sales or low single digits on R&D and have mid 80s gross margins and that compares to cognics which spends more like mid teens on R&D and has more like 70% gross margins and the other machine vendor peers spend a little bit less and have even a little bit lower gross margins than cognics and it really comes down to the way Key Ants goes to market and how they focus. They generally focus just on the middle to lower tiers of customers and then they're really trying to develop more standardized products that are going after the very high frequency applications and it's a very process oriented sales so they'll hire more college graduates compared to cognics and then they'll train them with the products, put them out in the field and it's more of a scripted sales process where they're keeping track of more those activity like KPIs and the CRM so it's how many calls are you making, how many shop visits, how many demos, it's really good coverage and really relentless. If you get on the rabbit holes become a meme in the community there's some pretty funny memes out there. If you're going to download a product spec sheet up the website you should use your buddy's email and phone number not your own. You'll never hear the end of it. That's the two main players. Maybe I'll briefly mention a couple other competitors while we're here. The other bucket I would say is China and these are primarily for manufacturers in China. Hycrobotics which is a division of hike vision is probably the third largest player in the industry overall and they've grown quite quickly driven by that domestic market with the domestic manufacturers and they're probably about half the size of cognics in terms of sales. There's a number of other smaller Chinese players as well and then outside of there there's more legacy players. There's within Telldine, there's Dalsa and Point Gray, there's Sick and Basler and Germany, Data Logic, Matrox was a company acquired by Zebra and there's even companies like MD Tech that just sell the software and then you can go get your own hardware. So that's kind of a lay of the land in terms of the industry structure. I did read quite a bit about Kians and their sales process and now effective of a sales organization they built but in the sale for someone like cognics where it sounds like it's much more technical built to spec perhaps advanced and not so much off the shelf. Are they selling to an Amazon who then works to integrate it into the other hardware and equipment that they're using or are they selling into the equipment manufacturers whoever builds the conveyor belt who is the customer for cognics? The answer is yes. If you think about how they're going to market 70% of sales are direct and that would be direct either to the factory floor where cognics and the automation engineers at the customer are working to implement the system but direct also includes selling to a machine builder or an OEM who would then integrate it into the machine and then they take that machine to the and factory floor and then the remaining 30% about half of that is going to be through systems integrators and that's primarily logistics at this point and then the other half of that 30% would be distribution and that's primarily just for end markets like Cambodia or something where they just don't have any presence that's kind of how I would separate the customer. That makes sense. It's an interesting dynamic within the overall industry structure conversation. Before we move on too far I do want to get into a bit of the history and what came across through the work that I was doing is that there is technical expertise here and there's a focus on that technical expertise. So can you bring us back to the beginning and the origin story here when they got into the market and some of the evolution over time? It's really important to understand for cognics. The DNA of them stacking escurves essentially over 40 to 50 years. The company was founded in 1981 and they came to market with what was the world's first industrial optical character recognition system. So again reading numbers and letters and at the time the system looked more like a big video camera with external processing and the first application was actually to read serial numbers on semiconductor waifers for IBM. That was the data man product family that still exists. From there they got a call from Johnson and Johnson to do optical character recognition for labels. They did that application but then J&J asked them hey can you do some of these other at the time what would have been novel applications like verifying the caps or on the bottles and the labels were present and the like. So that's when cognics added more of these inspection type of applications. The first 20 years or so of the business was pioneering machine vision for all these different applications but it was very much for semiconductors and the electronics capital equipment industries primarily. It was as high as 80% of sales going into the dot com bubble were two of those industries and that went to 54% following the hard way. Around 2000 was when we saw smart cameras really come onto the scene. So were you embedded the processing directly into the camera smaller footprint more ruggedized now that enabled the product to be ready for the factory floor as we more often think about it. So we saw machine vision get adopted in more heavy manufacturing industries like automotive consumer electronics food and beverage packaging. So by the end of 2010 that semiconductor and electronics capital equipment was down to 15% of sales as they really brought in the end markets that they could serve all those new escurves. Around 2010 was when cognics came out with their first ID product for reading bar codes. It was primarily aimed at displacing laser-based scanners in logistics facilities because I could do it faster and then with much higher read rates. If you go back in 2010 they were talking about they'd be happy if one day long-term target $75 million of sales from this line of business and then they worked very closely with Amazon to develop the technology in in 2021 the peak. It was about $300 million in sales and 30% of cognics is overall business. So turned out to be just an enormous new escurve for them. At present we're really in the early innings of the next tech evolution for the industry and it began in 2017 when cognics acquired VD and then in 2019 they acquired a company called Sua Lab and those were both essentially IP and Aqua Hires in the field of deep learning or the application of AI into machine vision. VD's co-founder and CTO is cognics's current VP of AI technology. So after those acquisitions there was a big development effort and they came to market with these new deep learning and edge learning products and the way I conceptualize this is moving from rules-based programming of vision systems to teaching by example which has large implications and I think we're probably in the first ending of this next chapter behind the scenes of a long cyclical down cycle. Could you paint that picture in terms of an example rules-based let's say a package is over a hundred pounds with these dimensions, send it in the left lane if it's under that send it through the middle if it's super small send it to the right and it's a pretty standard tree of logic and decisioning. Where would this new example or this new training come into play? Do you have any real world applications that would paint the picture? There's deep learning and edge learning as I mentioned deep learning first. So traditional rules-based vision is great for a lot of things but not for things very subtle or nuanced tasks with a great deal of variation. Those sorts of tasks you still usually have a human performing them because frankly it's just hard to quantify them to then program them. Maybe an example of that would be inspecting a phone case for very subtle defects. There's obviously a wide variety of colors and there's just an endless list of potential defects. So scratches, dents, blemishes and the paint and they're very small, very nuanced and then there's also usually a scale of what's acceptable and what's not where a human would be able to look at it and pretty quickly discern that but it's very hard to program a machine to be able to do that especially obviously high speeds. But with deep learning instead of programming it you teach it with very large datasets and very large image sets. These are good phone images, these are defects, these are acceptable defects and the machine learns what is acceptable and what is not and is able to accomplish that task just to quantify this a little bit. So there's currently still 30 million people in the world doing visual inspection and that's something that humans actually aren't great at. They can do it quickly but they get fatigued and they miss things. That's a big opportunity. Another example would be something like deboning chickens where every chicken's different, it comes down the line and different orientation, the wings are somewhere different. It'd be impossible to quantify and program a robot to be able to grab the right places but with deep learning that's an application that they can now do. Deep learning, the implication would be potentially new applications for machine vision. Edge learning is the other end of the spectrum. And it's probably more financially tangible for the investor community right now. These products, they come pre-programmed for specific applications, but then they're trained with just as few as five to 10 images. So it's essentially very easy to deploy relative to traditional rules-based vision. You can usually get an application up and running in a few hours. So the major implication here is that COGnex can now sell to less sophisticated customers. Again, thinking about where they've historically focused on the top of the pyramid. And they can do it with them much less technical sales for us, so broadening their customer base below where they've typically served. So now they have the product and COGnex is currently making a sales force investment called the Emerging Customer Initiative. And it's really taking the Keyents Playbook. These Emerging Customer Sales Nodes, they call their employees COGnewards, their salespeople sales nodes. They're hiring young salespeople instead of technical applications and engineers. They're arming them with these new edge learning products that are really easy to implement. And they're going after these less sophisticated customers, more SMB types, and even less sophisticated tasks with an existing customers where those exist. And then it's a much more formalized selling playbook that tracks the KPIs just like Keyents. They're getting compensated on both their selling activity as well as their commission. Last year, cohort one of these sales noise hit the field and they did 80,000 customer visits added 3,000 new customers to a base of what was previously 30,000 at a creative gross margins. And they accidentally year at about $1 million a week in sales from that first cohort. The second cohort entered this year in 2025. And hopefully we'll do a bit better just from lessons learned. Obviously, they're now competing in Keyents's bread and butter target customer set. So they are running into them sometimes with these customers being vended. But the majority of the time these customers have never used machine vision at all. They get more like 60 to 70% of their sales have been people that have never had a camera on their factory. Obviously, the financial implications here are they're trying to grow their customer base from 30,000 to hundreds of thousands over time, which could be obviously revenue opportunity and then at a creative gross margins and reduce the customer concentration as well over time. The customer base, I think you painted a picture just in terms of the various applications and who might use them. But what does it look like in terms of a customer, whether it's a contract, whether it's a sale, the ramping of that contractor sale, and then what the stickiness is, once you build something out. The stickiness of a customer is quite high. Once you're on the factory floor and the people that are managing the floor are familiar with the software used to program the cameras. And it's usually more or less standardized, especially for some customers with a, for the sake of simplification, they don't want a bunch of different machine vision, vendors out there in the associated software. It's a CapEx sale and they have, as I mentioned historically, been focused on the top of the pyramid customers. So they've been tied to the large CapEx buildouts of companies like Apple and Amazon. And that obviously comes in a wave and then it's more of a question of stacking new S curves and when the next waves and CapEx is gonna come. But the customers themselves are generally quite sticky. Maybe this is a good segue into the different end markets. And we can start with consumer electronics. It's interesting because that end market's tied to the CapEx spending around new consumer electronics or features. There was a tailwind from the adoption of cell phones and then there was the tailwind from Apple building out the iPhone manufacturing. At one point, Apple was as high as 20% of cognizance sales, which is pretty incredible. It's now probably down to more mid to high single digits range. There was another big tailwind when OLED came on the scene and that was with Samsung. So looking forward, it's interesting to think about if there will be new form factors or changes to the existing ones for consumer electronics tied to things like LLMs or if there's AR or VR is gonna take off ever or if humanoid robots are gonna take off. Basically, if any of those things were to be manufactured at high volumes, that would fall right into cognizance sweet spot. Sophisticated customers, big manufacturing capability and consumer electronics in 2024 were 17% of sales and longer term, they expect that market to grow mid teens as the target. Logistics as the largest end market is 23% of sales. At peak, it was 30% of sales and Amazon was up to 17% of cognizance sales. Obviously, there was the well publicized down cycle in that end market. They did return to growth in 2024. They grew 20% and that's the long-term target for that end market is that they can grow 20%. And Amazon probably is back around that high single digits to 10% range in terms of a customer. In cognizant fashion, they're stacking new S curves and logistics, which is really interesting. So they're taking the vision tunnels which they've really honed with Amazon to new customers and new geographies and those non-Amazon customers are growing very quickly. And then they're also identifying other applications outside of barcode reading, things like vision, inspection to see damage boxes coming down the line or damaged labels, make sure there are labels, even things like dimensioning to do estimating of your shipping costs and things like that. So they get their foot in the door and then they find additional applications within the end market. And that's the story of logistics at the moment. Automotive is the second largest market at 22% of sales. And theory, EV battery should be a good tailwind for them. Cognizance has solutions. The transition just frankly necessitates a bunch of catbacks whether it be new automotive lines but also battery facilities that otherwise would not have been the case if it was just new models internal combustion engine models. But frankly, the growth just didn't come through last year as people had expected it to. Automotive sales were down mid teens in 2024. It's not expected to be a good year in 2025, but it shouldn't be a deep decline like 2024 at least. And long-term, the target for this in market is 10% growth for Cogniz. Two other quick ones. So semiconductor is probably about 10% or 15% of sales now. They're selling primarily to the semiconductor capital equipment manufacturers. And the growth outlook there is positive at this point in time. And then the other 20% to 25% is general factory automation. And keeping an eye on PMI there, primarily, it's been historically long down cycle for industrial manufacturing. And that's been felt by Cogniz in that part of their customer base. Is there a average useful life in terms of the equipment that they're installing? For the equipment as well? Ideally, when a customer puts the camera in the factory, they're hoping it's going to last for 10 to 20 years. So there's not really a regular replacement cadence. It's more about you get the customer in your install base. And then they'll be doing brown field catbacks on existing lines. And that'll create revenue opportunities for Cogniz. Every now and then, they'll be these green field opportunities with those customers or just new in markets and new customers. And that's what drives the growth of the industry over time. On the software side of the business, is that an actual revenue driver for them when I think about how much they will make from the sale of equipment? Is it all recognized in year one? Or is it spread out with some type of software component? The joke I say is that Cogniz doesn't adjust out their stock base comp, which is great for a company that's essentially software company. But you get stuck with the cyclicality of an industrial company as a trade off. So it's all an upfront sale. The software is tied to the hardware catbacks. It sounds like you get the S curves when there is a paradigm shift or a new form factor introduced into an industry that would require new catbacks spent on a factory or a production line where new Cogniz equipment would have to come in. But if it's just producing the same phones or the same cars without added features, you're not going to see a big step up in revenue because you don't have to make adjustments to the factory. - I think that's right. You can think of the core business as supporting customers as they're doing their big catbacks spends. They're tied to that. So at the moment and consumer electronics, for example, we're keeping an eye out what it could be, but there's not something that's driving a big catback spend to Apple, for example, that and markets kind of out of more steady state maintenance type of level. You characterize it correctly. - Within that context on semi-conductors, it sounds like it's a meaningful chunk of the business, but not something that maybe has captured the same tailwind of the actual market itself with semi-conductors. What drives the disconnect there? What would you tie that to where it's not? Just this massive chunk of the business right now as we're seeing this cycle play out. - They actually did a bolt-on acquisition of a company called Moratex in late 2023. And Moratex sells optics and lighting. A lot of that goes into the semiconductor's end market. Prior to that, COVnex's semiconductor exposure was quite small, probably sub-five percent. And then after that acquisition, it's bigger. It grew quite quickly in 2024, for example. The expectation it's gonna grow very strongly again in 2025 at this point in time. So I think that kind of explains why it's the size that it is. - Yeah, everything you mentioned just in terms of the various big customers, the auto market being tepid or modest at best at the moment, all aligned. One thing we didn't get into when we talked about the history was the culture more so, the leadership. What would you point to just in terms of the evolution of the culture and the management team? - I think it's rare that you get to this point in the discussion before you talk about the culture at COVnex. It really is very unique. As I mentioned, their employees are called COVneids. And they've had two CEOs in the past four and a half decades. So the founder, Dr. Robert Schillman is one of a kind. He goes by Dr. Bob. And I was thinking about how to describe him quickly for a podcast. And I think there's one theory that he says tongue in cheek that I think captures him quite well. He says it tongue in cheek, but he says that he doesn't believe in exercise because basically your body is a bunch of mechanical joints and anything mechanical as a finite amount of use. And just from that you can see he's clearly an engineer. He's really smart. He's very skeptical individual and a bit rebellious. He's got a really good sense of humor. And he was also very intentional with culture from the very beginning. Cognix's motto is work hard, play hard, move fast. It's a very engineering centric. I believe they have the largest collection of PhDs and machine vision in the world working on just advancing the field. They hire really smart people and give them the room to make autonomous decisions. And they say, be right most of the time. They're willing to fail and try new things. But they also try to have a lot of fun when they're doing it. Some examples of that on leap years, a few employees are selected to go jump out of a plane with Dr. Bob. And I think he dresses up like a frog or something ridiculous. One year they rolled in an armored vehicle to deliver the cash bonuses and Halloween every year is a huge event. They have without question the best annual reports in the business. They're themed. So you should take a look at those. They're really fun. But what's interesting is they've really managed to maintain the culture from what I can tell. And I think there's two reasons they've been able to do this after Dr. Bob. So first they have what are called ministers of culture in every office around the world who are responsible for maintaining the culture. And it's actually an incremental job and addition to their existing job and they have meetings and they actually get a separate check in terms of compensation for this job. And it's viewed as very important to the company. So Rob Willett is the current CEO. He joined from Danahur in 2008 and became CEO in 2011. Dr. Bob stayed on as chief culture officer until 2021. He really insured the culture endured through the transition and Rob's really embraced it. That long handoff as well. I think is really interesting and unique. The fruits of the culture is obviously things like voluntary attrition is half of their industry peers. But I also think it's just critical in terms of being nimble and adopting new technology to stack all these new S curves over time. It ultimately comes back to the culture. There's no culture like a funky or fun engineering group. I think it's one of the more unique things out there in the market when you find one quite interesting to hear about. It did bring up one point just given the technical background. Is there a big patent portfolio or IP focus for the business? You can go on their website and they list hundreds of patents. I want to get to just some of the numbers on the business as well. I think we've danced around the bit. But if I'm thinking about what the cost of their equipment is to visualize for a factory purchasing these. Is there a general sense of what the average selling price would be? These new like edge learning products might only be one or two thousand dollars per system and a customer might need a couple of those. So at the low end of the spectrum, you can think of an order being as maybe ten thousand dollars or something like that. And then obviously at the high end of the spectrum, the individual sensor or vision system can cost over ten thousand dollars. And there could be many of them on a single implementation. So those orders would be running in the hundreds of thousands of dollars. And the turnaround time for something being contracted to actually being delivered. I would imagine there's a lot of lead time in terms of when they're building out of facility. What does that look like? Is it something where they have to be building this to spec when the customer gives them the order? Or do they have off the shelf stuff that they can deliver? The smaller purchase order is I think it's more book and ship type of business. Very short cycle and then their largest most strategic customers with complex cat-x plans and the like. There's more of a lead time. And then you could think of it as they're going to build the shell of the factory first and then the lines and then after that's when the machine vision cameras go on. Or they're selling to the machine builders that obviously have their own lead times. For those larger customers, I think they generally have not crazy visibility, but a little bit of visibility. Taking it down to gross margins and operating margins. Where do those typically hover and how much cyclicality is there? Longer term, they target 15% top line and constant currency and 40% incrementals. And then they layer on capital allocation from there, starting with revenue. So in the 10 year period ending either 2020, 2021, 2022. So just before this down cycle and adjusted for the best you should have it that they did, they've grown about 13% excluding M&A and then mid teens and constant currencies. So they've been right there. I think actually the right long term bogey and what most people have in mind, understanding where out of cyclical place. So a few years above this, but it's more like a low double digit top line longer term is how most people think about it. But again, we're at the end of a very long down cycle and there's this new customer base that's being added to cogniz that hopefully should drive some revenue, at least in the near term that's above that on margins. Similarly, they're depressed cyclically right now and also they have the headwind from the emerging customer initiative investment, the Salesforce investment. And they actually expect the emerging customer initiative to be operating margin accretive. It already is gross margin accretive. But operating margins last year were 13% and that's down from a peak above 30% and there's a couple hundred basis points of the emerging customer initiative had wouldn't there. But longer term, they target regaining 30% level as the leverage returns to the business. So they get back to growing and then obviously the Salesforce investment flips from a headwind to a tailwind to margins. That swing is pretty meaningful between the 40% incrementals 30% target 13% last year. I know we're in a down cycle right now, but when you go back over time is that the types of swings that you tend to see. Not to that magnitude. I think part of it is that they've invested very heavily in this current strategy, which makes it deeper than otherwise would have been. For example, back in 2019, which was prior to this down cycle the last time that they had a down cycle margins went from 27% down to 20%. The depth and duration of this downturn along with investing through it is what makes the operating margins what they are. And the target again is to regain the 30% level and then grow incrementally 40% from there. In terms of the cycle, are there signposts that either you monitor the industry monitors, the management team monitors to suggest that there's inflections and I know there's several and markets here. So it's going to vary, but what could you point to just in terms of the timing of that and what you typically look for. For logistics specifically, that market's return to growth now. So that's their largest in market. It grew quickly last year should probably grow quickly again this year. Similarly, semiconductor you watch the semi cap companies or the company selling equipment to TSMC and the like that's primarily who they're selling to and again, the outlook there is relatively positive right now consumer electronics like we mentioned. We're kind of at a steady place as we're waiting to see if there is going to be like a next consumer electronics type of cat, cycle that's needed and that could be new features or new form factors, tidal limbs or AR and DR or whatever it might be, but that's usually what drives the growth there. There's nothing tangible on the horizon right now for that and then automotive we found a place of stabilization now or we're closer to that than the down cycle, but just watching the automotive OEMs and their capex is the signposts that you watch for that one. And then lastly, PMI is the catch all for the rest of the business is just general industrial activity, industrial sentiment, the like is what drives capex for those types of customers. Would you classify their performance as somewhat of a leading indicator on cycles I'm just trying to visualize you have the capex announcements which will ultimately drive the business for cognics, but it does feel like they're still at the front end of the actual cycle where you will eventually see that come on board and whatever they're producing be released is that a fair way to categorize it relative to just general macro trends. And I think you've captured it right there given their short cycle and then especially with something like consumer electronics that business will reflect before we even know what it is they're spending on just the nature of that so it's pretty interesting in that regard. Definitely very, very interesting you see that a little bit in the transports, but this is a completely different play on something similar going back to the business you mentioned cyclicality you mentioned a little bit about investing through the cycle. And the balance sheet capital allocation more broadly how would you categorize them in terms of capital allocators and risk of managing the balance sheet versus conservatism first on free cash flow generation for them they've generally converted 100% of net income to free cash over time so they produce good free cash flow. And they've returned over 100% of that to share older so a third dividend and two third share repurchases they do do the occasional M&A and that's primarily to this point at least been more those IP slash aqua higher type of deal so VD and Sue lab and then they have a fortress balance sheet I would say so they net cash on the balance sheet cash and investments actually right now is 10% of the market cap they've always run net cash on the balance sheet. When you piece it all together cyclical business they won't argue that how do you do that. do you go about approaching valuation when you can see sharp changes in cycles, different dynamics like that? How do you approach it? I think there's two ways that I approach valuation on this one. So the first one is we look at what's implied in terms of the future free cash flow growth to justify the current valuation. So we do that by assigning an exit free cash flow yield out in the future and then have our internal hurdle rate, which is at least 10%. Then we place that within just the range of outcomes for the business to assess the attractiveness. But not present. We think frankly that cognizance to compound free cash flow more of like a low double digits rate over the long term. That's below the long term model and also coming from a place that's at the low point of a cyclical down cycle with a lot of hopefully margin recovery ahead. So that's the bowl case. The other way we look at it is the historic multiples and just given the margin dynamics, we specifically are looking at EBITAN that next 12 months sales is the relevant one for us. Currently, it's within reach of its 10 year low at five and a half times next 12 months sales. It traded as high as 16 times sales during ZERP, which is like most things, just hilarious and hindsight. But otherwise a more normal range. It's been more like six to 10 times sales is where it's traded. My takeaway from that would be that the market might be suggesting that there's some more meaningful compression on the margin profile of the business. We're certainly seeing it beaten up through the cycle. But is there any reason to believe that margin compression or the margin recovery won't be as strong as the cycle does recover? I think that's certainly a discussion. Historically, we have seen margins decline in magnitude with cycles and they've always recovered back to 30%. We just saw even in the last quarter where some growth returned to the business and they grew more and the double digits organically. You can see the leverage that fell through in the quarter. There are signs like that that we're watching. That's certainly a debate on the name is what normalized margins will look like. Always the debate on anything in this space. I can appreciate that. What are the other risks you would point to? I think we probably outlined a lot of fairly obvious risks. But what really stands out to you? There's a couple that are top of mind. The first one is obviously just the cyclicality. Early is the same thing as wrong. Basically, every incremental investor in cognizance the last year or two is probably feeling very early right now with regards to that one. Frankly, it's just a matter of if the cycle will turn or if we're sitting here a year from now and still feeling early, hopefully still temporarily. The other one is China, which we briefly mentioned. It's 18% of sales in 2024. Two-thirds of that is to Western multinationals and a good chunk of that. Probably half of that are more as Apple Foxconn. For those customers, it seems unlikely they're going to put high robotics in their factories for obvious reasons. For the remainder, call it the other mid to high single digits, percent of overall cognizance sales that are domestic Chinese manufacturers. I think on a five to ten-year basis, it's probably going to be an uphill battle to grow those customers at least. That's a well understood risk as well that people talk about. The last one would just be the tech transition. Ultimately, I think the tech transition opens a great window of opportunity for cognizance and there's reasons to believe they're early and ahead in terms of applying machine learning to machine vision. But with any tech transition, it opens the window for disruption as well. So that's the third risk out there that's on my mind. Yeah, it's an interesting cyclical name because when I think of the majority of cyclicals, they will trade in terms of the slow down or the growth within a range. It is not this S curve type growth when you do have major pickups. And to the extent that they do have new S curves emerge, which from the very, very, very high level view, I would imagine that they exist out there and it'll just be a matter of how cognizance aligns. It's an interesting one with a slightly different tilt on cyclicality than normal business. What's also interesting is the cyclicals, whether it be analog simmies or simmicabs or like metals and mining, they'll trade peak multiple entroff earnings and the like. And you look historically at cognizance as multiple and it's basically trended up when sales are growing and down when sales are declining. It's kind of really interesting. I don't know exactly why that is, but that's the reality. The peak on peak phenomenon is one that can be painful on the way down. So it's interesting. To watch. This has been very informative, filled in a lot of blanks that I had in terms of the research that I did and there were many. What would you point to as the key lessons that take away from cognizance and apply elsewhere? It's funny. I was thinking about all the episodes I've listened to a business breakdowns and I feel like it's got to be the number one answer is culture. I'd be curious if that's accurate, but I think it's clearly culture for cognizance too. And I think what's interesting about it more than just having a unique culture is how they've maintained it through the founder, departing. So specifically, like I mentioned, the ministers of culture that they have the job of maintaining it. Secondarily, just that long overlap of the founding CEO with the new CEO as the new CEO took over the reins and just ensuring that it lasted through the transition. I think those were unique and I hadn't really seen that before elsewhere. It's definitely one of the more common answers often related to businesses that have had these very long-term durations of success. So it's interesting and very hard to measure, which probably makes it even more valuable to study. So very fascinating business. Thank you again for sharing the knowledge, Brett. It was a pleasure. It was a lot of fun. Thanks for having me back on. To find more episodes of breakdowns ranging from Costco to Visa to Moderna or to sign up for our weekly summary check out join Colossus.com that's JAOIN c-o-l-o-s-s-u-s dot com. [Music]