Specialized AI brains for physical industry

Everyone wants a piece of general purpose models. Instacart has deployed ChatGPT for recipes and meal planning. The Mayo Clinic is using it to summarize patient records. Schneider Electric is using an OpenAI LLM to generate sustainability reports. With such powerful models, what’s the need for specialized models built for specific industries, especially in climate tech? In this episode, Shayle talks to Sam Smith-Eppsteiner, partner at Innovation Endeavors. She recently wrote a blog post arguing that there may be a market that general purpose models struggle to meet: physical industries where training data is siloed, unstructured, and private. She talks through climate-relevant examples like Cadstrom’s copilot for electrical engineers, Hubflow’s automated trucker scheduling, WeaveBio’s AI-powered platform for regulatory approvals. Shayle and Sam also cover topics like: Applicable cases, like cross referencing complicated technical manuals, repetitive manual work that employees dislike, and technical compliance The technical knowledge lost when workers retire and how specialized AI could help What it takes to build specialized models, including data access, vector embedding, prompt engineering, and fine tuning What budget categories businesses might use to pay for specialized models Selling the technology (i.e. the traditional SAAS model) vs selling the work (i.e. answers informed by models) Recommended resources Innovation Endeavors: Specialized brains for industry: the immense potential for domain-specific AI Innovation Endeavors: The next industrial unicorn: Where is AI rapidly transforming the physical economy Catalyst: The coming robotics wave Latitude: How utilities are designing and embedding AI operating models Credits: Hosted by Shayle Kann. Produced and edited by Daniel Woldorff. Original music and engineering by Sean Marquand. Stephen Lacey is executive editor. Catalyst is brought to you by Anza, a platform enabling solar and storage developers and buyers to save time, reduce risk, & increase profits in their equipment selection process. Anza gives clients access to pricing, technical, and risk data and tools that they’ve never had access to before. Learn more at go.anzarenewables.com/latitude. Catalyst is brought to you by EnergyHub. EnergyHub helps utilities build next-generation virtual power plants that unlock reliable flexibility at every level of the grid. See how EnergyHub helps unlock the power of flexibility at scale, and deliver more value through cross-DER dispatch with their leading Edge DERMS platform, by visiting energyhub.com.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:01Speaker 00

  Latitude Media, podcast at the frontier of climate technology.

• 0:07Shayle Kann?

  I'm Shail Khan, and this is Catalyst.

• 0:11Speaker 02

  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 anecdote on both sides. I don't think we know exactly where this is going to net out.

• 0:30Shayle Kann?

  Coming up, we ask the David or Goliath question on whether it makes sense for there to be specialized vertical AI solutions for physical industries.

• 0:47Speaker 03

  Catalyst is brought to you by Antenna Group, the communications and marketing partner behind the most influential companies in energy, climate, and infrastructure. For three decades, Antenna has helped ambitious companies stand out, build market leadership, and become category-defining brands. From breakthrough startups to global industry leaders, their team brings the expertise, relationships, and track record to accelerate your momentum. Learn more at antenna group dot com.

• 1:13Speaker 04

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• 2:20Shayle Kann?

  I'm Shayle Khan. I lead 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 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. So obviously, AI is going to transform some of these industrial

• 3:05Shayle Kann?

  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? My friend Sam Smith-Epstein has been thinking about this question a lot. She's a partner in Innovation Endeavors, which is another early-stage venture firm. And

• 3:48Shayle Kann?

  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.

• 4:04Shayle Kann?

  Sam, welcome.

• 4:05Speaker 02

  Awesome. Thanks for having me, Shayle.

• 4:06Shayle Kann?

  Excited to finally have you on and talk about AI in physical industries and industrial sectors. I don't know, whatever we want to call it. Why don't 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?

• 4:30Speaker 02

  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, anecdote on both sides. Like, I know there are some sort of general purpose, really high-growth AI tools that I hear 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 anecdote on both sides. I don't think we know exactly where this is going to net out. 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

• 5:14Speaker 02

  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 in materials, construction and the built environment, supply chain logistics, all these categories, what we're talking about. The data has a few features for these customers. So first, it's 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

• 5:54Speaker 02

  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 like, very fragmented data structure that where the data is a 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 siloed.

• 6:27Shayle Kann?

  That is especially true in these, in fiscal industries? Like, is it more true that the data is siloed and 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?

• 6:47Speaker 02

  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 are using like contemporary software. As opposed to software, like if you look at, I think we were looking at once. 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 nineties, maybe even the eighties. 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.

• 7:26Shayle Kann?

  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, 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. What I think about it is it's the list of 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 exist and are siloed and have lots of PDFs and stuff like that. So that's one. What's the next one?

• 8:04Speaker 02

  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 like the PDF component of this. And it's a lot of textual data, which you know, actually can work quite well with LLMs such as maintenance logs and other things. But it's also just a massive amount of sort of like 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.

• 8:30Shayle Kann?

  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 ChatGPT 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?

• 9:04Speaker 02

  Well, I 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 like the public domain where OpenAI 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 they haven't seen enough of them.

• 9:33Speaker 02

  And so you can imagine like AEC, like Architecture Engineering Construction is a particularly good example.

• 9:39Speaker 02

  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 text based or video

• 9:51Speaker 02

  data that's on the internet sort of pretty broadly.

• 9:54Shayle Kann?

  Yeah, I thought that this one, the walled garden data is is a very strong point, certainly from what I've seen. And like I experienced 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 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, like, 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 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,

• 10:39Shayle Kann?

  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.

• 10:54Speaker 02

  Exactly. And I, you know, 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 like 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 a one performant and two defensible product for the category.

• 11:23Shayle Kann?

  Yeah, that's like the, that's the barrier you need to break through. Like this, this, your thing that you're building needs training data. If the training data is all private and walled, you gotta be able to use your customers' data to train. They're not gonna like that and you gotta figure out how to get over that hump. And if you can't get over that hump, there's kinda no way you end up building a product that scales or gets better.

• 11:44Speaker 02

  I think that's like true, yeah.

• 11:46Shayle Kann?

  Okay, so, so legacy systems of record, all this unstructured documentation, and then the data is private, it's a walled garden. What else?

• 11:56Speaker 02

  So those are the main things on the like 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, you know, the alternative, again, sort of general tool. I think there are a couple of demand side factors, but I, 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 that, 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, 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

• 12:40Speaker 02

  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 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.

• 12:57Shayle Kann?

  Yeah, I mean, that obviously, that's that's true in a bunch of sectors. It's one of these things, though, that I do think is a big macro driver, but is not like an 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 painkiller, but it's like a slow release painkiller. You know what I mean? So like, yeah, people are probably feeling the crunch of like the lost expertise over time. But it is really, it just like happens so slowly. You're like a frog in boiling water.

• 13:29Speaker 02

  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, a demand for product based on that need. But I but I hear your point. It's sort of like a very long term trend.

• 13:51Shayle Kann?

  OK, 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. So how do you think about the sort of cost to build something bespoke like this?

• 14:14Speaker 02

  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, 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.

• 14:49Speaker 02

  Because prompt engineering, obviously, is cheap, scalable, 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 are you actually building your own model? I think I'm hearing really widely variable things there. I'm hearing founders say 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 a natural evolution of prompt engineering. And I've heard other folks saying, we're going to build our own model. And that's just part of what we need to do in this category. I think the reality is 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 to accomplish a pretty simple task, actually has nine separate agents

• 15:31Speaker 02

  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. You might break this up into many different pieces and build your own model for like 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 will continue to do so in terms of how exactly you build this. And it's going to be different based on the problem. But also, I think we're going to see a lot of evolution here. And my hope is that, you know, like DeepSeek and others have shown, there is a path to sort of lower cost, smaller models that are viable for startups to build.

• 16:17Speaker 03

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• 18:27Shayle Kann?

  Okay, so let's assume the premise holds. There is reason to exist for specialized verticalized AI tools of various kinds in physical industries. The question then is 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, what are the categories of types of companies to be built here? The first one that I know you've referenced is, I think, maybe the most obvious one, which is the knowledge base. It's like 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.

• 19:10Shayle Kann?

  So I think of this as just being a really, really smart encyclopedia that is specialized to the individual needs of the individual sector, the individual customer. Am I thinking about that one right?

• 19:22Speaker 02

  I think it's more than encyclopedia. I think it's like making sense of complexity. So, um, if 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 incomplete data. And I think a really important part of that is like, again, data across systems and marrying that. Um, so like, maybe one very simple example that I'll give in sort of construction land, um, 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

• 20:06Speaker 02

  that against likely blueprints or schematics or whatever that show exactly what fixture we're referring to, and then taking that understanding of which SKU we're referring to and looking that up in your supply chain system. So we're talking about probably a few different sort of types of documents, a few different systems, and marrying that data to answer what's a relatively simple question, actually. But today requires someone to, probably requires multiple people to look into multiple systems to like 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. Like, 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 or encyclopedia, but it is understanding and making sense of complexity from all the mess that is this data here.

• 20:50Shayle Kann?

  Yeah, it's like a sentient encyclopedia or something. It's like Encyclopedia Brown, not an encyclopedia. There you go. Reference my favorite book as a kid. Um, 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 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 is, is toward figuring out what these agentic solutions are going to look like. And we're, it feels

• 21:34Shayle Kann?

  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, a trip with like all the reservations that I need, that kind of thing.

• 21:51Speaker 02

  Yeah, I mean, so this is, you know, AI agents, agentic AI, you can think about it as like a layer or system of action. I think like think manual tasks broadly here, but in my mind, it's, there, 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 there where it's like highly variable or time sensitive, like if you have super seasonal swings in the amount of labor you need to do something, like that seems like a natural

• 22:32Speaker 02

  fit for something you wouldn't want to sort of staff up against. So I, 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 warehouses at certain times, there need 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 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

• 23:17Speaker 02

  party in a transaction. There actually is a company conduit automating on the other side. So we may in fact have conduit and Hubflow sort of chatting to schedule an appointment. But that's the kind of example of the kind of workflow we expect to get automated really. Can you 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 huge amounts of opportunity. I think the question will be often the workflows folks are starting with are, you know, narrow in nature. So the question is, do you sort of build depth in capturing value in that workflow? Do you build additional workflows? You can think about this as like just like much, much better

• 23:59Speaker 02

  and more flexible and like less customized RPA, right? Just much better, much better performance.

• 24:06Shayle Kann?

  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, which is basically, I guess you call them co-pilots for discovery, for engineering and discovery. And we've talked about it more in a discovery context because we've spent some time on like AI for materials discovery, which is an interesting sub niche of this, this whole universe. Less, less on the engineering side, where I know you spent a lot of time and I find pretty interesting as well. But yeah, I mean, how, how bullish are you on this, like using AI to do better discovery, or I guess design is maybe the other component of this?

• 24:42Speaker 02

  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, it's pretty wild. And I think it actually speaks to like, we may even see multiple big companies in a given category. Like in software engineering, you're seeing folks who are helping you with your existing codebase, folks who are helping you with new projects, folks who are helping you translate from one language to another, that those are, might all be different 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 off their plate? So I have an early investment in a company called Catstrom. They're building

• 25:25Speaker 02

  a co-pilot for electrical engineers on the PCB design side. They're starting on the verification and validation end of things, like checking downstream as opposed to sort of design upstream. What's good about that is engineers often don't enjoy doing that. I 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 layer cake they're building here is actually quite replicatable to sort of other engineering domains as well, which 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

• 26:09Speaker 02

  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.

• 26:25Shayle Kann?

  I like your framing of just like, look where the, 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, in these sectors, for sure.

• 26:35Speaker 02

  Yeah, I think what's hard is like, there isn't 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, it's a natural starting point, right? Where it's like, augment them by starting with a thing they don't want to do. 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?

• 26:58Shayle Kann?

  Right, right. Okay, and then the last category is one we haven't talked about much here, but I've, 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, you know, compliance in a lot of these sectors, but particularly the heavily regulated ones, of which energy is one, is a, 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.

• 27:36Speaker 02

  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 have to comply with even internally? And it's a total, you know, 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 or management of operations. And then potentially also relevant for design support on new projects. So you can imagine, I'm sure you've seen plenty of this yourself, like in energy development,

• 28:17Speaker 02

  this is relevant in thinking about citing 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.

• 28:30Shayle Kann?

  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 citing optimization thing for developers. It's like, great, like, I do think there is value in that. 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, you can, 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 moat by doing what you do.

• 29:12Shayle Kann?

  Or is what we're going to end up with this like suite of hundreds of relatively small companies solving a real problem, but not with a big enough market opportunity to be like, you know, generational category defining type companies?

• 29:25Speaker 02

  I don't know. And I think we'll have varied answers here by sector, but I think one way we've 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 like 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 customers, 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, you know, 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. 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.

• 30:07Speaker 02

  So we, this is very like analog from a different part of our portfolio, but we have a portfolio company called Weave Bio, so from the health and bio space 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 they're significantly speeding up the time it takes to prepare one of these media applications and improve 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 in unto itself, a big, large opportunity. There are other plays we've seen though, where that first work Flow, whether it's compliance or agentic 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 going back to, for example, scheduling between trucking and receivers.

• 30:52Speaker 02

  My understanding is that there are fines for if you're late for missing your, you know, delivery appointment. Can you actually then automatically schedule those fines and actually apply them 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 like a much larger TAM right there. And additionally, you potentially could build it sort of obviously 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?

• 31:25Shayle Kann?

  Yeah, I think that often it does come down to like, what is the, clearly the team as well, but it it often comes down to like, what? 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?

• 31:42Speaker 02

  Totally.

• 31:43Shayle Kann?

  Okay, so I guess final question for you. Stepping back, I mean, the other thing that's interesting about this, this, you know, 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 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. Is it? Is it a standard software as a service type offering? Or is there innovation there?

• 32:29Speaker 02

  Yeah, so I think that the Choices in a very black and white sense are sort of sell the work or sell the technology, right? So selling the technology is sort of traditional SaaS model you're talking about of offering your AI product to the user to use on their own. Selling the work is saying, hey, I'm just gonna actually do this whole task for you. I'm gonna consume the AI myself and sort of obfuscate whatever 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.

• 33:14Speaker 02

  And especially if that company is going to staff up, right? 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 gonna provide me an account manager or a case manager and someone who's experienced in the space and is gonna 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 will the technology broadly, and I think this is something we should all potentially be thinking and talking about more. But 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 gonna be 90% margin SaaS. Like, we may be talking about 40% to 70% gross margin based on inference cost.

• 33:59Speaker 02

  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, if you're also on top of, now we have inference cost is significant, layering on services to deliver 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 opposed to selling technology, there's often a much bigger sort of dollar value at stake in doing that, which I think relates to...

• 34:39Speaker 02

  A couple of 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 in actually seeing this play out. The AI companies could actually draw from labor budgets as opposed to software and technology 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 like plays out in practice, I think, you know, we've yet to see.

• 35:20Speaker 02

  And then I think, which we touched on before, the other piece here is just like, in these categories, 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. Like, 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 gonna lose their job because they have a tool that like 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.

• 35:54Shayle Kann?

  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 gonna have to prove over time, but ultimately will become incredibly valuable and sticky, which is, you mentioned tapping into the labor budget. I think actually ultimately, some of these things can tap into the capital budget, into the capex 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 gonna 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

• 36:38Shayle Kann?

  hard time upfront convincing a customer that that is gonna be true, and so you have to come in with some other thing and then prove that one.

• 36:46Speaker 02

  Yeah, but yeah, I was gonna say, I think the capex Piece is a longer term play, 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 capex or facility is going to last for 10 years longer, which you won't know for 30 years.

• 37:17Shayle Kann?

  All right, Tam, this was awesome whirlwind tour of AI for physical industries. I've been loving the stuff you've been putting together on this, and it's fun to talk through it with you here. So, thank you.

• 37:26Speaker 02

  Yeah, thanks so much for having me. It was fun.

• 37:29Shayle Kann?

  Sam Smith Epstein is a partner at Innovation Endeavors. This show is a production of Latitude Media. You can head over to latitudemedia.com for links to today's topics. Latitude is supported by Prelude Ventures. Prelude backs 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 Marquand. Stephen Lacey is our executive editor. I'm Shayle Khan, and this is Catalyst.