Catalyst with Shayle Kann - Specialized AI brains for physical industry

Starting point is 00:00:01 Latitude Media, podcast at the frontier of climate technology. I'm Shail Khan, and this is Catalyst. There are some sort of general purpose, really high-growth AI tools that I hear are losing some deals to, you know, vertical-specific players because they are better suited to that context. But on the other hand, like, I know law firms that are using Anthropic instead of Harvey or legal AI tools. So there's definitely innate data on both sides.

Starting point is 00:00:28 I don't think we know exactly where this is going to net out. Coming up, we ask the David or Glythe. question on whether it makes sense for there to be specialized vertical AI solutions for physical industries. When utilities need flexible capacity they can count on, they turn to Energy Hub. Energy Hub works with more than 170 utilities, coordinating over 2.5 million devices to manage 3.4 gigawatts of flexibility built for the moments when utilities can't afford uncertainty. Energy Hub builds and operates virtual power plants that utilities actually stake their grid planning on, coordinating EVs, batteries, thermostats, and more through a single platform built for utility scale.

Starting point is 00:01:14 Predictive, verifiable, and designed to perform when it counts. Learn more at energy hub.com. Trillions of dollars are flowing into clean and critical infrastructure, but those investments aren't driven by technology alone. They're shaped by markets, by policy, by capital, and by the institutions that connect them. I'm Alfred Johnson, CEO of Crux, and host of a brand new podcast, Critical Capital. Each episode, I talk with people deploying capital, shaping policy and building the clean economy.

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Starting point is 00:02:19 I'm Shayal Khan. I leave the Frontier Strategy at Energy Impact Partners. Welcome. So as most of you know, I think, my job is to invest in early-stage companies that are delivering some true step-function improvement in technology in categories like energy. and industry. And in other words, I'm primarily a hardware investor. At least there's always some hardware in the loop

Starting point is 00:02:42 and the types of things that I get involved with. And more broadly in climate, the sectors that matter, the ones that drive the emissions and thus matter from a climate perspective, are generally doing big physical things. They're doing industrial chemical reactions or generating terawatts of primary energy or moving big things long distances, et cetera.

Starting point is 00:03:03 So obviously, AI is going to transform some of these industrial processes, or at least how they're performed. And we talked a few weeks ago about one flavor of this, which is replacing some human labor with robotics. But another flavor is using AI to augment the humans and make them more productive or efficient or safer, or whatever it might be. And here there's kind of an interesting question, which is, do we need domain-specific, verticalized models, tools, and companies, or will the big foundation models, the names that you've been hearing about, raising eye-watering, staggering, sums of money, will they eventually just get good enough to essentially do all this stuff for us?

Starting point is 00:03:41 My friend Sam Smith-Eppsteiner has been thinking about this question a lot. She's a partner in innovation endeavors, which is another early stage venture firm, and she's been investing in companies in this verticalized space that she calls specialized brains for industry. I think it's super interesting. It's a very dynamic question, and Sam has some great thoughts on it. So let's hear. Here's Sam. Sam, welcome. Awesome. Thanks for having Michelle. Excited to finally have you on and talk about AI and physical industries and industrial sectors. I don't know, whatever we want to call it. Why do you start with just like the high-level thesis? You've been spending a lot of time in this space. Walk me through at the highest level. How are you thinking about where there is value in the applications of this new wave of AI to these big, heavy industrial industries?

Starting point is 00:04:32 Yeah, so I think the first thing we've been thinking about is sort of like whether and why and where. there will be sort of like specialized vertical applied AI products versus using sort of general tools here. So I think there's like no perfect answer. We're hearing, you know, anac date on both sides. Like I know there are some sort of general purpose, really high growth AI tools that I hear are losing some deals to, you know, vertical specific players because they are better suited to that context. But on the other hand, like I know law firms that are using Anthropic instead of Harvey or legal AI tools. So there's definitely anecdotate on both sides. I don't think we know exactly where this is going to net out.

Starting point is 00:05:09 I think my theory is that there is room for and a real need for actually sort of like vertical applied AI tools in the industrial and physical economy categories. I think there are a few reasons why. I think most of them live on the sort of like supply side of this of, you know, why technology and how technology will serve this sector well. And this is mostly about the customer's data. So like if we think about the customers here across a bunch of different sort of industrial categories. So think manufacturing, natural resources like mining and materials. construction in the built environment, supply chain and logistics, all these categories, what we're talking about. The data has a few features for these customers. So first, it's

Starting point is 00:05:45 fragmented and siloed. So it lives in these sort of legacy systems of record. So, you know, in manufacturing alone, you could imagine you have an ERP, an MES, a PLM, and sort of many more. And these systems are often sort of like legacy. They're not sort of, you know, contemporary software tools, sometimes they might even be on-prem. And again, it's across all these different systems. So they have this very fragmented data structure where the data is like conceptually linked, like it has theoretical relation, but it's not sort of pragmatically or practically tied. And beyond that, there's just tons of PDFs here, right? They live in SharePoint. They live in email. They live in totally different kinds of documentation storage. And so this data is just inherently sort of fragmented and silo. That is especially true in these

Starting point is 00:06:32 in physical industries? Like, is it more true that the data is siloed in PDFs relative to other industries, or is it just that that's, like, true universally? And so there's, per that criterion, there is an opportunity for verticalized things kind of anywhere.

Starting point is 00:06:48 I think there's probably some of it everywhere, but I think more of it here. And I think the tooling, again, is more legacy as opposed to, like, I don't know if you imagine you're a large tech company. You've probably built a lot of your own tooling or using, like, contemporary software, as opposed to software. Like, if you look at, I think we were looking at once,

Starting point is 00:07:07 you know, companies that have built sort of multi-billion category defining products selling to hardware engineers, and the last one was started in like the 90s, maybe even the 80s. So there's just not a lot of contemporary products here compared to what you imagine the tech stack or sort of back office stack even looks like for a parallel technology company. Right. Yeah, that makes sense to me. I mean, they're also just, I would bet the average age of the company is older than the average age of the company and obviously in software. Okay, so we're getting through this list that you published a really good piece on a little while back, which is like the list of characteristics, the way I think about it is it's the list of

Starting point is 00:07:45 characteristics that make it true or that would make it true for there to be an opportunity for a specialized, verticalized, AI solution for that industry. So the first one that we've been talking about is like these legacy systems of record that are that exists and are siloed and have lots of PDFs and stuff like that. So that's one. What's the next one? So the next one is sort of related, which is a lot of that data is unstructured. And so that has to do primarily with the PDF component of this. And it's a lot of textual data, which actually can work quite well with LLM, such as maintenance logs and other things.

Starting point is 00:08:17 But it's also just a massive amount, sort of highly technical and visual data. So you can imagine sort of blueprints, schematics, diagrams, even 3D models, all this kind of stuff, which looks quite different from a lot of what AI models have been trained on so far. far. And is the premise there that like, okay, so you take a bunch of, you have unstructured data in a bunch of different places. And then, and what we're, what we're kind of asking ourselves here is, is that a reason for there to be something specialized? Or could you take all that unstructured data and all those PDFs and just upload them to whatever generic LLM, upload them to Anthropic or chat GPT or whatever it's going to be? And it'll synthesize them just as well. Like, is there a reason why unstructured data is better suited to, a particular, like a specialized model? We think this relates maybe actually to the third point, which is most of this data is private. So most of the data is private and proprietary and lives on these customers cloud in a best case scenario, maybe on-prem, but certainly not in sort of the public domain where Open AI has trained on it. And so I think the reality is these models are just not that good today at sort of understanding, you know, a very technical diagram, a blueprint, because it's,

Starting point is 00:09:31 they haven't seen enough of them. And so you can imagine like AEC, like architecture, engineering construction is a particularly good example. Because there's a lot of geometric and spatial representations, you need to understand that design intent, proprietary training data. And most of that doesn't look like, you know, the sort of normal tech space or video data that's on the internet sort of pretty broadly. Yeah, I thought that this one, the Waldgarden data is a very strong point, certainly from what I've seen. seen, and like, I experience a lot of it in the energy industry, right? In the energy industry, like, so much of the data is private. And this has been an issue in various software-type businesses that have been tried to be built around the electricity sector in particular. It's

Starting point is 00:10:15 like, ah, but if only we had all of the utilities internal data, then we could optimize X, Y, or Z. It's like, great. Good luck getting utilities internal data. It's super sensitive data. They're not going to share it with anybody. So it's definitely true. And I can see how that is a a hindrance to a generalized model because they don't have access to any utilities data that's like that. And so if you can get access to sufficient volume of it in a verticalized context, then you certainly have an advantage versus the generalized approach. Now, still getting access to it in that context is no small feat. But if you're building something for that sector and only for that sector and it's private, then maybe you've got a better shot of it, I guess, is the concept.

Starting point is 00:10:54 Exactly. And I was just talking to a founder in one of these categories, and she was stating that really important, I think, to her, was from the beginning. She made sure that access to that data and ability to train on it was a part of every MSA she signed. And at the very beginning, it was really challenging, actually, to get customers over the line on that, but now it's become pretty boilerplate for her. But she feels like it was an incredibly important part of her building one performant and two defensible product for the category. Yeah, that's like the barrier you need to break through. Like this, your thing that you're building needs training data. If the training data is all private and walled, you've got to be able to use your customer's data to train. They're not going to like that.

Starting point is 00:11:38 And you have to figure out how to get over that hump. And if you can't get over that hump, there's kind of no way you end up building a product that scales or gets better. I think that's like true. Yeah. Okay. So legacy systems of record, all this unstructured documentation. and then the data is private. It's a walled garden.

Starting point is 00:11:56 What else? So those are the main things on the data side, which I think, again, lead me to believe that a specialized product, we can talk about that's the model itself or something else, will be more performant than the alternative, again, sort of general tool.

Starting point is 00:12:10 I think there are a couple demand side factors, but I think really the main thrust is what we just talked about. On the demand side from the customer piece, I think one thing we're seeing is, you know, the great crew change, which applies to a lot of these categories where there's a bunch of sort of skilled experienced workers,

Starting point is 00:12:26 whether that's field engineers, field technicians, all these kinds of things who are reaching sort of age of retirement, and there's not enough folks to fill the gap behind them. And that means two things. One, we actually just need to do more work with fewer people in the future as we have sort of a potentially smaller labor force to do the same work and or that knowledge is actually retiring. And so we need a way to capture that expertise

Starting point is 00:12:48 and sort of make sure we have a lot of that locked in into a product. given that today it lives in people's heads, and that's how these systems sort of operate over time. So that's one big piece. Yeah. Yeah. I mean, obviously, that's true in a bunch of these sectors. It's one of these things, though, that I do think is a big macro driver, but it's not like an immediate – it's like climate change in some ways. It's like it's going to take decades to play out, and that's like one of the problems with it is that there's no immediate – you're not – it's like a pain killer, but it's like a slow release pain killer.

Starting point is 00:13:17 You know what I mean? So, like, yeah, people are probably feeling the crunch of like – the lost expertise over time. But it's really, it just like happens so slowly you're like a frog in boiling water. Yeah. I mean, we were looking at something in oil and gas in the space that in talking to customers, it was highlighted as like a major, major pain point for them of like a real fear. So I think there, we are at a point where at least customers are voicing to us in some categories,

Starting point is 00:13:46 a demand for product based on that need. But I hear your point. that it's sort of like a very long-term trend. Okay, and then the last one you listed in the piece was a good point, which is like, is more the question of like, can you build it? Can you build it economically, I suppose, is the question. And when I say it, I mean something specialized and verticalized and good enough to compete with whatever, however the performance would be of a generalized solution trying to do the same thing.

Starting point is 00:14:10 So how do you think about the sort of cost to build something bespoke like this? Yeah, so I think the question is how? you build it and what needs to be true there. So I think on the technical side, there's no perfect answer, but in talking to founders who are actually building, it seems like prompt engineering can get you quite far, right? So like it's widely variable, but let's say you can get to like 70 to 90% of where you need to be from a performance perspective. I think the question is whether that's sufficient for the task at hand and whether like you're going to consume the AI internally or the customers are going to fill the gap with human in the loop.

Starting point is 00:14:50 Because prompt engineering, obviously, sort of cheap, skillable, all those kinds of things. I think the layers beyond that would be, are you embedding, are you doing vector embedding, are you doing fine-tuning, or you actually building your own model? I think I'm hearing really widely variable things there. I'm hearing founders say, you know, embedding is important. We're doing it, but it doesn't work very well for non-textual data. I'm hearing some folks say, like, fine-tuning is sort of like a natural evolution of prompt engineering. and I hear other folks saying, we're going to build our own model,

Starting point is 00:15:18 and that's just part of what we need to do in this category. I think the reality is, like, we're the very early innings of this, and there are lots of ways to sort of break up these problems. I know one company that, too, accomplish a pretty simple task actually has nine separate agents doing sort of micro parts of the task and doing sort of compliance and testing of the answer on the task in the first place. So you can imagine this is actually – it's not like you have one mega monolith model to solve even a relatively small workflow,

Starting point is 00:15:48 you might break this up into many different pieces and build your own model for a tiny portion of it, fine-tune something else, and just prompt engineer another part of it. So I think folks are getting creative and we'll continue to do so in terms of how exactly you build this and it's going to be different based on the problem,

Starting point is 00:16:03 but also I think we're going to see a lot of evolution here. And my hope is that, you know, like deep-seeking others have shown, there is a path to sort of lower-cost smaller models that are viable for startups to build. virtual power plants are becoming a reliable way for utilities to manage capacity. But enrolling devices is just the start. What really matters is confidence, knowing those resources will perform when dispatched, and being able to prove it from the control room to the living room. Energy Hub's platform handles the full picture, from near real-time forecasting,

Starting point is 00:16:38 locational dispatch, and the kind of rigorous verification that holds up when regulators, grid operators or leadership ask, did it deliver? Easy enrollment creates momentum, proven performance builds trust. That's why more than 170 utilities rely on Energy Hub to manage over 2.5 million devices delivering 3.4 gigawatts of flexible capacity. See what that looks like at energyhub.com. We're living through a profound economic shift, and energy sits at the center of all of it. Trillions of dollars are flowing into power plants, transmission lines, battery factories, data centers, but the future of energy isn't shaped by technology alone. It's shaped by markets, by policy, by capital, and by the institutions that connect them. I'm Alfred Johnson, CEO of Crux,

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Starting point is 00:18:10 Whether you need to position yourself as a thought leader in between project announcements or translate complex ideas and technologies into tangible, compelling stories that resonate with the media, F-Tankpr.com. Check out fish tankpr.com. That's F-I-S-C-H-Fish-Tankpr.com. Okay, so let's assume the premise holds. Like, there is reason to exist for specialized, verticalized AI tools of various kinds. in physical industries. The question then is like what kinds of companies can you build, basically? And so I want to talk through because you've also spent a bunch of time thinking through, like, what are the categories of types of companies to be built here? The first one that I know you've referenced is just, I think maybe the most obvious one, which is like it's like the knowledge base.

Starting point is 00:19:03 It's like using it as a, using AI as a better knowledge base. It's to solve the problem of all the data and all the information being siloed in a million different places or held by people who are retiring or whatever. So I think of this as just being like a really, really smart encyclopedia that is specialized to the individual needs

Starting point is 00:19:21 of the individual sector, the individual customer. Am I thinking about that one, right? I think it's more than encyclopedia. I think it's like making sense of complexity. So you want to think about it is like, I think people are just wasting a lot of time today, searching for information, manually cross-referencing sources or making decisions based on complete data.

Starting point is 00:19:39 And I think a really important part of that is, like, again, data across systems and marrying that. So, like, maybe one very simple example that I'll give in sort of construction land is say you're on a construction project and your question is, you know, when are the light fixtures that are going on the second floor coming in? It's a really actually quite easy question conceptually, but today quite hard to answer because we need to do a few things. We need to process that natural language of understanding what light fixtures on the second floor means and referencing that against, like, blueprints or schematics or whatever that show exactly what fixture we're referring to, and then taking that understanding of which skew we're referring to and looking that up in your supply chain system. So we're talking about probably a few different types of documents, a few different systems, and marrying that data to answer what's a relatively simple question, actually.

Starting point is 00:20:26 But today requires someone to probably requires multiple people to look into multiple systems to figure out what those light fixtures are, find someone else who's in procurement to go into the supply chain system and figure out when it's coming in. That's a not hard question, but it is challenging actually to answer given the way the data is structured and permissions and stuff across all those different systems and understanding. So I think it's more than just search for encyclopedia, but it is understanding and making sense of complexity from all the mess that is this data here. Yeah, it's like a sentient encyclopedia or something. I can see Encyclopedia Brown, not an encyclopedia. There you go.

Starting point is 00:21:00 I reference my favorite book as a kid. Okay, so that's one category. Then the next category, I guess, goes a step further, which is more in like the agentic world. It's like the, you know, AI, you call it AI that does the work, basically. So that also seems, I guess, on its face fairly straightforward. It's like, I guess in your example, it's like, I don't know, go buy me another set of light fixtures for the second floor or something like that. And it goes off and does it for you, right? Which is like, it seems like that's the general direction of travel of the, of AI.

Starting point is 00:21:32 is toward figuring out what these agentic solutions are going to look like, and it feels like we're kind of at the cusp there, but not in the broader AI world, like consumer AI world. We're like on the cusp and, you know, it's starting to pop up a little bit. We haven't totally cracked it. Like, I still can't have somebody go book me a trip with, like, all the reservations that I need, that kind of thing. Yeah.

Starting point is 00:21:55 I mean, so this is, you know, AI, agents, agentic AI. You can think about it as sort of a layer system of action. I think like think manual tasks broadly here, but in my mind, it's, there is something interesting here. I think the value could be in cutting time or cost, but also potentially improving the end outcome. And I think, like, what's a good fit is anything where the work is annoying, like where the person who's doing it actually doesn't like doing it, finds it frustrating for whatever reason, is where the work is already error-ridden. I think that's very common where you have to reference lots of data sort of across systems and it's a multi-step process. and R.Barrie, it's highly variable or time-sensitive. Like, if you have super-seasonal swings in the amount of labor you need to do something,

Starting point is 00:22:34 like that seems like a natural fit for something you wouldn't want to sort of staff up against. So I see this more thinking about sort of like especially repetitive manual things. So one particular example on the sort of automating repetitive manual tasks piece is a company called Hubflow. What they're doing is sort of automating scheduling and supply chain and logistics. So automating that scheduling between truckers and receivers, trucks need to come into which. houses at certain times. There mean to be resources deployed against that. Today, that's done through emails, texts, whatever systems folks live in today. And what's interesting is Hubflow is automating that for the trucker. But the third party, the receiver, actually has the same exact

Starting point is 00:23:12 workflow. Like, to them, it's invisible, which I think is an interesting part of what AI agents can do here, which is like really play a role for sort of one party in a transaction. There actually is a company conduit automating on the other side. So we may, in fact, have conduit and Hubflow so chatting to schedule an appointment. But that's the kind of example of the kind of workflow we expect to get automated. You can imagine there's tons of these up and down the stack of work that gets done in all these industrial sectors, whether it's payment scheduling, this kind of automation here. So we just think there's a huge, massive opportunity.

Starting point is 00:23:48 I think the question will be often the workflows folks are starting with are narrow in nature. So the question is, do you sort of build depth in capturing value? that workflow? Do you build additional workflows? You can think about this as just like much, much better and more flexible and like less customized RPA, right? Just much better, much better performance.

Starting point is 00:24:08 Yeah. Okay, so then the next category that you've laid out is the one maybe we've talked about the most actually on this podcast, which is basically I guess you call them co-pilots for discovery, for engineering and discovery. We've talked about it more in a discovery context because we've spent some time on like AI

Starting point is 00:24:24 for materials discovery, which is an interesting sub-nitch of this whole universe. Less on the engineering side, where I know you spend a lot of time and I find pretty interesting as well. But yeah, I mean, how bullish are you on this using AI to do better discovery or, I guess, design is maybe the other component to this?

Starting point is 00:24:45 Yeah. I'm quite bullish. I mean, you just look at what's happening in software engineering, right? I mean, I think the way Cursor and tons of other software engineering tools are taking off was pretty wild. And I think it actually speaks to, like, we may even see multiple big companies

Starting point is 00:25:01 in a given category. Like in Software Engineering, you're seeing folks who are helping you with your existing code base, folks who are helping you with new projects, folks who are helping you translate from one language to another, that those might all be different

Starting point is 00:25:11 sort of companies doing that. So I think this is like really similar to agentic AI of how can we do work better, faster, cheaper? And actually, can we take the parts of the engineering work that engineers don't enjoy doing

Starting point is 00:25:21 off their plate? So I have, have an early investment in a company called Katstrom. They're building a co-pilot for electrical engineers on the PCB design side. They're starting on the verification and validation end of things, so like checking downstream as opposed to sort of design upstream. What's good about that is that engineers often don't enjoy doing that and have to spend a lot of time sort of error checking their stuff. So it does like delight the user in that way. And I think the layered cake they're building here is actually quite replicatable to sort of other engineering domains as well, which is

Starting point is 00:25:55 is they're understanding the intent of the engineer, but then parsing the information on the individual sort of components of the board and what the limits are, they're then sort of combining all that to understand how a board would theoretically work, and then from there, understanding what simulations need to be run to make sure that the system actually does operate that way, and then to actually automating those simulations. Like that same sort of layer cake of work, I think, could be applied to lots of other spaces. So, I mean, you can see there's Augmenta when it comes to like electrical system design. We imagine there'll be plenty more here in the real world so structural engineering and sort of other domains as well.

Starting point is 00:26:28 I like your framing of just like look where the work is annoying, and you've probably got, that's a good beachhead at a minimum. And there's a lot of annoying work in these sectors, for sure. Yeah, I think what's hard is, like, there is a natural tension here if you are selling technology to the end user where, like, they don't want to be automated away. So I think that's a natural starting point, right, where it's like augment them by starting with a thing they don't want to do.

Starting point is 00:26:54 and like maybe over time you do unlock more and more of the work, but it's like how do you get that end user sort of delighted and bought in as opposed to afraid? Right, right. Okay, and then the last category is when we haven't talked about much here, but I've seen certainly firsthand in the energy sector as well, which is more AI for like compliance and broader risk mitigation, I suppose, as well, where compliance in a lot of these sectors, but particularly the heavily regulated ones of which energy is one,

Starting point is 00:27:21 is a big hidden burden. A lot of people, a lot of work, a lot of time, a lot of documentation. And so it seems one to me that is kind of obviously ripe for some degree of automation to the extent that it is trustworthy. Agreed. I mean, industrial companies, you just have huge amounts of compliance, both to your point on the regulatory side, but also just even internal. It's like, what are your standards and policies and operating procedures that you

Starting point is 00:27:50 have to comply with even internally? And it's a total cost center bottleneck headache. And I think it's a really good fit actually for AI, right? It's really textual in nature. So I think the work here is to sort of parse and understand the rules. And then some combination of sort of understanding your work and mapping it against those rules, real-time monitoring and flags are management of operations, and then potentially also relevant for design support on new projects.

Starting point is 00:28:16 So you can imagine, I'm sure you've seen plenty of this yourself. Like in energy development, this is relevant. thinking about siting, optimization, and permitting of how do I even choose the right sites where I feel like I could, you know, get away with a permit here or there's a good path forward, let alone sort of downstream and actually managing it. That example is a good one to raise, like, a broader question that I often have, which is like there are some places where intuitively it makes a lot of sense to introduce these kinds of solutions because it is labor intensive and annoying and so on. But on the other hand, they're just, they don't seem like that big an opportunity. This is how I feel about the siting optimization thing for developers. It's like, great. I do think there is value in that.

Starting point is 00:28:56 I don't think you can build an enormous venture-grade business just doing that. And so the question is always, you know, am I missing something about like, look, this is a beachhead. You're entering with this thing, and then you're going to broaden out with those existing customers or with other customers. You're going to build some kind of data mode by doing what you do. Or is what we're going to end up with this like suite of hundreds of relatively, least small companies solving a real problem, but not with a big enough market opportunity to be like, you know, generational category-defining type companies? I don't know, and I think we'll have varied answers here by sector, but I think one way we've

Starting point is 00:29:34 thought about it, too, is like, how sticky are you? I think what's hard about the upstream stuff, like citing and permitting, is most of the data you're using is external, right? It's sort of third-party to understand sort of the land and permitting situation as opposed to sort of internal to the customer. And when we looked at that category, when we talked to customer, or it sounded like they were sort of readily changing tools, which is less likely to happen if you're sort of deeply integrated with a tool and they're building a model off of your own data that is already sort of highly performant and be hard to sort of transition to another player.

Starting point is 00:30:04 I also think it's a question of just like how big that specific opportunity is and how much build opportunity there is on top of it. So we, it's a very like analog from a different part of our portfolio, but we have a portfolio company called Weave Bio, so from the health and biospace, that for pharma and therapeutics is helping them, to build INDs, which is, you know, a step in the first stage of the FDA approval process. And there's significantly speeding up the time it takes to prepare one of these media applications

Starting point is 00:30:28 and improved quality. We actually feel like, well, one, that's, again, heavily integrated into your own data and systems in a different way. But two, that is actually unto itself a big, large opportunity. There are other plays we've seen, though, where that first workflow, whether it's compliance or gentic AI, seems small. But it's like if you could embed into the payments layer that sits on top of that, then maybe that actually becomes quite significant. So coming back to, for example, scheduling between trucking and receivers, my understanding is that there are fines for, if you're late for missing your delivery appointment, can you actually then automatically schedule those fines and actually apply them

Starting point is 00:31:05 and then capture some of that flow through? Like, if you can start to embed into transaction layers that sit on top of the workflows you're on, then all of a sudden, obviously, you're talking about a much larger tam right there, and additionally, you potentially could build it sort of obviously get other workflows on top of that. So I think the question is just like, how big is the problem? What can you build on top of it? And like, you know, as it always comes back to it, it's like, what's the ambition of the team and where do they want to take it? Yeah, I think that often it does come down to like what is the, clearly the team as well, but it often comes down to like what would be the next thing? What can you imagine coming after this? And is there a next thing with this customer base? Or do you have to believe in some other version of expansion that's like harder to picture? Totally. Okay, so I guess final question for you, stepping back, I mean, the other thing that's interesting

Starting point is 00:31:50 about this revolution of all these new AI companies is that there is some innovation on the business model and what they're actually selling because they're coming from a world where like software companies sold, there was a pretty clear thing software companies would do, and it's usually software as a service, and you charge a subscription, and it's like seat-based or utilization-based or whatever, and then you stack up your ARR numbers, and you've, You've got all your metrics. In AI world, there's at least some discussion of kind of turning the pricing model, at least on its head. I'm curious what you've been seeing from all these companies that are out there doing AI in the physical world.

Starting point is 00:32:27 Is it a standard software as a service type offering or is there innovation there? Yeah, so I think the choices in a very black and white sense are sort of sell the work or sell the technology, right? So selling technology is sort of a traditional SaaS model you're talking about of offering your AI products. to the user to use on their own. Selling the work is saying, hey, I'm just going to actually do this whole task for you. I'm going to consume the AI myself and sort of obfuscate whatever,

Starting point is 00:32:54 human in the loop or services are needed on top of that as part of that service. I think where we're finding that to be most interesting to customers is where they are already outsourcing. So if you're already outsourcing your compliance work, for example, to a third-party consultant, you're probably sort of willing to outsource that to a different company instead. And especially if that company is going to staff up, right?

Starting point is 00:33:20 So that's why the question is. Like, what kind of services are you actually providing on top of the technology? But if you're actually going to provide me an account manager or a case manager and someone who's experienced in the space and is going to effectively be my new consultant, but by the way, they have technology behind them now, then, like, maybe that could work. I think this will have interesting implications to margin profile. actually as well with technology broadly, and I think this is something we should all potentially be thinking and talking about more.

Starting point is 00:33:48 Like, inference cost is non-trivial. I think for a bunch of the AI companies, both in our portfolio and that we've seen, you know, this is like not going to be 90% margin SaaS. Like, we may be talking about 40 to 70% gross margin based on inference costs. And obviously there are ways to manage that and hopefully costs will go down over time. But that alone, I think, will make these businesses actually different than the companies we've seen before. But coming back to sort of business model, you are also on top of now we have inference costs that significant layering on services to deliver

Starting point is 00:34:16 a full service to your customer. This could be sort of like a much lower profile, margin profile business than we've seen before. That being said, obviously, the trade-off typically between like a full-stack business and a technology business is what you trade in margin you get in top line. So if you're selling services as opposed to selling technology, there's often a much bigger sort of dollar value at stake in doing that, which I think relates to a couple themes I'm seeing for adoption here that are interesting. I think this is like maybe one of the first times where hopefully, although I think we're like early and actually seeing this play out, the AI companies could actually draw from labor budgets as opposed to software and technology

Starting point is 00:34:58 budgets. Like I'm sure as you've seen, these industrial companies often sort of have limited willingness to pay for software. It's not, you know, always the highest priority, not something they've budgeted for, but they spend a lot on labor. And so if there's an ability to say, hey, I'm augmenting your labor force, making them more productive, cutting costs, et cetera, and I can actually tap into that budget. That's a much sort of larger pool to pull from. Whether that actually sort of plays out in practice, I think we've yet to see. And then I think which we touched on before, the other piece here is just like in these categories,

Starting point is 00:35:29 you definitely need an enterprise sales motion. Like you need top down, you need to manage complex stakeholders. But I think this is one where AI can delight the end user. So the person's actually like sitting with a tool every day. If you can take on that annoying work and just make their life better, then you no longer have that tension between, oh, the CEO sees ROI, but the process engineer is afraid they're going to lose their job because they have a tool that they love and makes their life better every day. And so ideally this is a way that things can actually move faster in terms of a sales process, too. Yeah, I mean, the only thing I'd add to that, I think this is really difficult to sell a priori. And so it's like a thing that companies are going to have to prove over time, but ultimately will become incredibly valuable and sticky, which is you mentioned tapping into the labor budget.

Starting point is 00:36:10 I think actually ultimately some of these things can tap into the capital budget and to the KAPX budget because, for example, for like applications with regard to maintenance, maintenance is like a huge cost, right? Or, you know, if you think, if you can end up making a case that you're going to extend the lifetime of existing assets, and so the capital budget can decrease by 10% because you don't need to replace some existing assets as often, like those are where the really, really big numbers come in, but you have a really hard time up front convincing a customer that that is going to be true. And so you have to come in with some other thing and then prove that one. Yeah, but yeah, I was going to say, I think the CAPEX piece is a longer term play,

Starting point is 00:36:52 right? Because that's effectively a value-based pricing model. And the value accrues over a long time versus labor, you can prove kind of immediately at least. So it is still some sort of sort of value-based pricing-related model, but if you can show that you can do the same work with 20% of the people, you can maybe take a week or two to do that as opposed to, you know, improving your CAP-X or facility is going to last for 10 years longer, which you won't know for 30 years. All right, Tam, this was awesome whirlwind tour of AI for Physical Industries. I've been loving the stuff that you've been putting together on this, and it's fun to talk

Starting point is 00:37:27 through it with you here. So thank you. Yeah, thanks so much for having me. It was fun. Sam Smith Epsteiner is a partner at Innovation Endeavors. This show is a production of Latitude Media. You can head over Latitudemedia.com for links to today's topics. Latitude is supported by Prelude Ventures.

Starting point is 00:37:43 Prelude Beck's Visionaries, Accelerating Climate Innovation that will reshape the global economy for the betterment of people and planet. Learn more at Preludeventures.com. This episode was produced by Daniel Waldorf. Mixing and theme song by Sean Marquan. Stephen Lacey is our executive editor. I'm Shale Khan, and this is Catalyst. Thank you.

Catalyst with Shayle Kann - Specialized AI brains for physical industry

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