Episode Transcript
[00:00:00] Speaker A: I think it's productivity. I mean, companies are looking for better ways to produce more products with fewer defects in the shortest amount of time. It's just capital efficiency.
[00:00:16] Speaker B: Gentlemen, we're back. I feel like it's been a little too long. One of the things I've got to bring up is today, this week we've just been seeing endless amounts of earnings. Figma went public today. There's a few other things Greg, you were just telling me about. Is it Meta, Microsoft? Are the markets responding to AI finally? Is that what we're seeing?
[00:00:35] Speaker C: Yeah, it seems like the IPO market has opened, which is good news for startups. And then I think like what earnings came out yesterday from Microsoft and Meta and it sounds like they had huge, they called it blowout quarters for both. And what a lot of people are speculating is like AI is starting to show its first real ROI for the big tech companies.
And I don't know if this number is exactly right, but Microsoft had forecasted $80 billion in annual capex for data center build out and they've increased it to like 120 or 130 billion. So like Excel, a lot of people thought that that capex spend would decelerate and that there was a big speculation that, you know, none of this stuff was going to pay out. But at least this quarter looked like there's real money coming back to these at scale companies and they're starting to see it hit their bottom line.
[00:01:22] Speaker B: And I feel like at some point too we're just starting to see the ecosystem is starting to be rewarded all of a sudden. Figma going public, these other companies starting to go public, the tech ecosystem is at balance again and thankfully will we have Greg who's monitoring the situation on X perpetually when it comes to all of these things. So thank you Greg.
Will, are you excited about this or are you just still trying to take in the fact that Greg got a haircut five minutes before this?
[00:01:50] Speaker A: You know, I was taking that fact in. I trimmed up a little bit expecting to, you know, enhance my, my looks a little bit and then realized there were all these grays in there. I want to believe it's a filter you guys put on, but no, I'm, I'm really excited about all of these things too. One of my claims to fame is I've just got a bunch of group chats with people in the tech industry, people in manufacturing, people in all sorts of, you know, just parts of the tech ecosystem. And as all of these earnings are being released and the announcements are coming out about Microsoft spending potentially 30 billion on CapEx just next quarter.
One of my friends was like, great day to be Will Drury. So I think what we're building is really just in that vein.
It's really just aimed at getting companies the equipment they need so that they can seize the moment for times like these.
[00:02:44] Speaker B: I love it. Well, let's dive right in and let's do a quick introduction. So good morning, good afternoon, good evening. For those of you that are joining, welcome to the Movers and Makers podcast. My name is Blake Menezes and with us is Greg Smythe and Will Drury, the co founders of Diagon. If this is your first time joining, be sure to check out some of our last episodes with Patrick McGee from the Financial Times and a bevy of other topics discussing all things that manufacturers and factory builders need to know in 2025. Gentlemen, we've got a full lineup of topics. Everything from Jensen was on stage at the All In Podcast Summit, which Greg is our resident all in subscriber. So if anyone from the All In Podcast ever listens to this, please send him a hat or something.
And we're going to dive right into some factory announcements that we've been seeing, discuss a little bit about the one big beautiful bill, which I truly hate the name of it, but at least there's some good stuff in there for manufacturers and what we're seeing at Diagon as it relates to AI. So let's dive into some of these factory announcements. I'm going to give a quick run through gents on this. Hyundai opened a $12 billion EV plant in Georgia. Panasonic is beginning production, a Kansas battery Gigafactory. Boeing is expanding a production line. That's a 1 billion investment. In South Carolina, GM is partnering up with Redwood Materials. We know those guys on battery recycling. And then US is imposing a 93% tariff on Chinese battery graphite.
Between that and Jensen dropping all kinds of numbers, around $500 billion in AI supercomputer investment just being built in Arizona alone. What does all this mean, Greg? I think you've been tracking, tracking this the closest. You're the person that's monitoring the situation. So give us all the tldr. The too long didn't read for everybody.
[00:04:33] Speaker C: There's probably a few factors. The other news that came out Yesterday was the Q3 GDP number was 3% and I think that surprised a lot of people. Like Q2 is neg negative. And a lot of that was driven by like the tariff, you know, the reconciliation day and the tariffs that happened then, you know, there's a real pullback in GDP then and a lot of people thought that that would maybe continue, but it looks like it's rebounded. And I think you're starting to see some of the tariff deals getting announced sequentially in the heat. With China cooling down, I think it looks like businesses have increased confidence to spend money building factories in the US again. And so some of it's just headlines. You need to be careful until they actually start cutting spending money. But it still seems like business confidence that, you know, the economy's starting to re accelerate again and the risk of tariffs and trade don't seem to be as bad as it was originally speculated in Q2. Yeah. So it seems to be increasing optimism around that and I think you're seeing that in these announcements.
[00:05:32] Speaker B: Will, any thoughts on this as you're seeing all of this? I know just a couple podcasts ago you were saying that factory announcements. What was it exactly? I think it was something about factory announcements impacting the market. What was that line?
[00:05:44] Speaker A: I think the line was like, if I had a dollar for every factory announcement, I'd be a billionaire. Something like that. Yeah. I think the factory announcements are cheap, but I think factory openings and starts of production are the things that we should be looking at. The market also typically rewards financial payoff and revenue that comes from things.
So the fact that so many companies like Meta and Microsoft are up on that news is really showing that it's not just announcements that are floating the market, but really the results starting to come in from these things.
With regards to the big factory openings, one thing that's striking me is that this market is really just being driven by the big tech companies, the hyperscalers. And I think that everybody really knows that those are the big companies that drive the stock market.
But one thing that I've been just thinking about is what does this mean for the startup ecosystem? You know, in the early 2000s, we saw that there was just lots of activity in venture capital, lots of deals happening, lots of companies getting funded to, you know, build new startups in the ecosystem, because that's where the perceived innovation happens. Like all of these hyperscale companies once started out as small companies, but I think we're seeing less of that activity these days. And I think just in my observation of the market, it's really going to take these companies building out the infrastructure. Companies like OpenAI and Anthropic, Microsoft, Meta, Google, they're really all building out this infrastructure that these companies are going to then build the application layers on top of them. And I think that's the really exciting thing for me. We haven't quite seen that trickle down effect yet, but I think that that's something very real in the startup ecosystem. And yeah, it's, it's going to be interesting to see how that manifests over the next few years.
[00:07:39] Speaker B: Okay, so what you're saying is, is we're starting to see, start starting to see some of the outputs of this AI craze. We're seeing that in the public markets. We started with that and we're seeing it kind of ripple into other parts of the business, other parts of the, the general, the US as a whole, even with some of these factory announcements. Greg, I' you were watching Jensen's talk at all in quite a bit. I know you were literally just mainstreamed into it. IV'd into your arm of all things, all in podcast updates. So Jensen was on stage and he was giving kind of the rundown of what the impact is to the US from a manufacturing perspective as it relates to these GPUs being built. Who benefits from this? What does this look like for manufacturers, for manufacturers of equipment? Give us kind of the rundown of that.
[00:08:26] Speaker C: Yeah, I think there's multiple sides to it. I think one of the downsides to the AI infrastructure is like besides construction there's not, I think a huge, like it can have huge revenue opportunities but like most of these data centers are lights out operated, you know, there's very few jobs that come from them besides like building them. And even though the numbers are ginormous, like largely that those numbers are going to Nvidia to buy more GPUs. So I think like even some of those things from a traditional manufacturing would create hundreds or like at the Tesla factory worked out, employed almost 10,000 people across like all the shifts. And that's not true in this like AI context for building out these data centers. The part I do find interesting is Jensen talks about these data as AI, as manufacturing intelligence, which is a really interesting thing where it's like traditional software sort of got run on the cloud but like it was just easily produced or not produced but like not like AI has to be manufactured like and produced every time you make it and it's going to be. Yeah. Anyways he describes it as this more like manufacturing process which I thought is interesting. And he also describes this like every factory will have an AI factory. And I think like the best example of that is Elon at Tesla in Austin. You know they have one of the biggest factories in the US but they have a huge data center there with some GPUs from, from Nvidia, but also Dojo, which is like their proprietary GPU they built just for Autopilot. And so you kind of have these two worlds starting to merge together. And I think there's, there's some model for that where you have every company is going to need or manufacturing company is going to have a factory, building something, but then maybe adjacent to it they have their own dedicated data center for whatever the application is. And for Tesla that's Autopilot and maybe they'll outsource it to the super scalers or the big cloud providers. But it's really interesting to think about. It's like manufacturers. You said there'll be two factories for every factory, one for building the thing and then one for managing the knowledge of the actual product or for the actual manufacturing process.
[00:10:29] Speaker B: What I love about this is it's starting to come back to a thesis that's been shared before.
The person who said it is evading me right now, which is data centers in a sense are factories. There are production lines in the way that data centers work. You could argue quite a bit of the general ways that data centers operate. And so I think it's true what you just said there, Greg, of the juxtaposition between one factory or what was it, two factories for every one, or there's going to be other factories that will need to come up. Some of those factories might actually be just very well built out data centers to even accommodate this. And I think we're seeing the market start to accommodate to that from the Microsoft earnings and a few other pieces.
Will, did you have any thoughts?
[00:11:11] Speaker A: You know, I just want to make sure that like this is grounded in some sense of like an understanding of exactly what we mean when we say that. So when I think about like this, the factories that are data centers, this is meant really in kind of an abstracted way where really the primary input to the data center is energy. You need energy to drive all the compute power that people are putting in with these queries.
Give me recommendations on what to do in Mallorca. And the output is really the result of all of those queries. And then there are lots of other kind of side inputs. People talk about water and coolant that are required, but those are really all in service to just keeping the temperature of that factory down. And so I think that we mean that in one sense, like these data centers really are truly factories of Information.
And this is really the first time that we've thought about computing power in that kind of way because it's being done at such a massive scale. But what I think Greg is getting at is a different thing where you know, factories in the sense of like factories that are making a physical product, they've got all types of information and data that they are spitting off all the time, like productivity uptime of your machines, error rates and things like that, images. You know, a lot of products that are made have like an X ray scan of every single product that comes through it. You know, that helps to keep up your quality and everything.
But data is expensive. So just imagine now you've got a factory that's producing, you know, hundreds of millions of batteries or hundreds of millions of iPhones and you've got to figure out what to do with all this data, how to process it. I think that's really the exciting thing is that you know, there are also these factories are trying to figure out how are they going to put their data to use. So I just thought that was worth like clarifying that there's, we're talking about two different concepts but in a very real way. Factories that make the physical products are now becoming, now requiring data centers to be on site and co located.
[00:13:22] Speaker B: What do you think's driving this shift? Do we think it's recent legislation, the just general momentum that's happening from all things AI and the inertia that's occurring from behind that? What's the primary one? Just quick answers, Will, what do you think? Greg, I'm going to go to you lightning round. What's your take?
[00:13:37] Speaker A: I think it's productivity. I mean companies are looking for better ways to produce more products with fewer defects in the shortest amount of time. It's just capital efficiency.
[00:13:47] Speaker C: Yeah, I think it's the same. I think in some ways you just won't be able to compete without AI in the short term. And I think that's why you just won't be able to compete in that context. And Jensen talks about it in that talk that you know, we don't need to onshore sneaker manufacturing or clothing and those things, but the strategic industries we do. He's like the only way we do that is through robotics, automation, AI. And it's like it is these separate things where like you're generating AI in a sort of in a metaphorical or in a digital sense but to make physical things you need AI to do it in you know, this factory of the future we often talk about And I think for critical industries, it's the only way. You just won't be able to do it economically any other way.
[00:14:27] Speaker D: Next generation manufacturing is going to be insanely technology driven. Robotics technology, AI technology. You're going to have factories that are going to be orchestrated by AI orchestrating a whole bunch of robots that are AI building products that are effectively AIs.
[00:14:42] Speaker B: Right?
[00:14:42] Speaker D: So you're going to have this layers of inception and the amount of technology necessary to create that is really insane. We've I, I love President Trump's vision, bold vision of re industrializing the United States. That entire band of industry that's missing. We outsource too much of it, frankly, we don't need to insource all of it, but we ought to bring onshore the most advanced, the most economy sustaining, driving national security, enhancing parts of the industry.
You know, people always degrade down to tennis shoes. We don't have to go there. We just manufacture chips and AI supercomputers. In Arizona and Texas we will in the next four years probably produce about half a trillion dollars worth of AI supercomputers. That half a trillion dollars worth of AI supercomputers will probably drive a few trillion dollars worth of AI industry.
And so that's only in the next several years.
[00:15:39] Speaker B: So I've got to segue just a little bit to the one big beautiful bill only because I have to wonder if some of these newer announcements, maybe some of that may have been the final straw that actually helped the camel's back to make some of these factory announcements. And I'm curious about it. Do you think or will, do you think that led to some of that?
[00:16:02] Speaker A: I mean, if I'm really honest, I think that it's probably still too early to tell what the impacts are going to be directly from the bill. A lot of the policies haven't actually gone into play yet. And so really I think we're still at this stage where companies are trying to figure out what does this legislature mean for me and for my company, how can I take advantage of it? I think that that's really the stage that we're at. So yeah, I think it still has yet to be seen. And I want to bookmark a question for you, Blake, because you've done probably more research than all of us combined on the big beautiful bill and some of the benefits that come out of that. So if you don't mind, actually I've got that question bounced back to you. What are some of the things you think manufacturers should be looking out for? That are going to be consequential for their businesses.
[00:16:51] Speaker B: So here's the three things you should know about this bill. The one big beautiful bill, if you are a manufacturer, if you are involved in any kind of a factory in the United States at this moment, there's three portions. I won't get into the exact sections, but the three things you want to know about are one, the 100% immediate expensing for manufacturing equipment itself so the machines that are making the products.
Two is 100% depreciation for factory facilities themselves. So there's a whole new provision around bonus depreciation. Dive deep into. We actually have a blog post on this you can dive into and read after the podcast. And then three is the advanced manufacturing credit. And so this is going to impact certain industries specifically, but this is toward the end of the year and this is going to be jumping from 25 to 35%. That alone when you start thinking about these numbers in the 7, 8, 9 figures, as you're thinking about factory investment or even beyond that, this has real impact for some of these manufacturers. And I think the factory announcements that we looked at earlier, whether automotive, aerospace or even defense related, which is what a lot of the provisions tend to focus around, specifically defense and aerospace, there is real impact that we will start to feel probably within the next year or two. And Greg, I'm curious to hear your take because I know you're always thinking about the lead time involved. If you make a decision today about what you're going to buy, what does that actually look like for impact both inside of that factory, that production line and the people that need to manage it. And so what's your take on some of the lead time as we think about the impact to manufacturers from some of these things? Is this a couple year time horizon? Is this six months? What do you think?
[00:18:27] Speaker C: These are complicated decisions. Like Will and I have been in the room and been a part of how these multi tens of millions of dollars or even billions of dollars decisions get made. And it's complicated, you know, to say it plainly. There's just like a lot of factors that could consider lead time is part of it environment like government incentives, tariffs are included in it overall business environment like demand for your product. And I think all of these factors and I think it kind of gets distilled down to like business confidence or something like that where it's like how does the CEO or the senior executive team feel about spending tens or hundreds of millions of dollars? I do feel like some of those things are coming together. Like GDP is like a sign of economic growth now. You've got some big government stimulus, uncertainty around incentives, Tariff situation is somewhat stabilized. And so I think as those things kind of come together, if the consumer holds up, then I think you will start to see accelerating decisions made.
And you could see it. I think there's a chance I won't give away my hot take, but, like, you could really see a lot of activity start to happen in Q4 and Q1 where like, there's like an overwhelming consensus, like, all right, we're kind of back to a booming economy, GDP is robust, and like, everybody starts doing everything at once. In a way where we've kind of talked about it, there's huge amounts of investment money on the sidelines. There's a lot of capex that's kind of locked up in this, like, lack of confidence or uncertainty. And it's pretty common where all of a sudden that all gets unlocked at the same time or within a relatively short period of time. And that can actually have negative consequences on lead time when like, everyone goes to try and buy something at the same time, like, no one's been trying to buy anything for two years and then they all try and buy it, you know, in the same quarter.
So that's kind of part of my hot take that I think some of those things could really heat up in Q4.
[00:20:15] Speaker B: We're keeping the hot takes till later. We'll start. Start brewing on yours as well. We're going to share the what's. What's the hot take of. Of the day with the podcast. But I think that's a really great perspective to share. What you, just what you just said. And I think it's important to consider too, how some of these things will continue to impact downstream for both manufacturers, factory builders, and even the people that are making the equipment that's going to be going into the factory itself. And I think we're continuing to see different headwinds in that way shifting.
So just to kick things off, as we're thinking about Diagon and as we're thinking about what Diagon is building, I know we're cooking up some things in the AI space. As the marketing guy, I'm a little biased to saying let's share a little bit more, but we are working on something that's going to be really changing the way that people think about searching for equipment and the people that make that equipment, the suppliers of that equipment, and it's going to be a mix of a little bit of AI Proprietary data and a few other things. But how are you starting to see this impact our company? In different ways. You mentioned briefly that I love to create a cloud custom project for every query and question.
But Will or Greg, who wants to dive in first on that one?
[00:21:27] Speaker A: You know, I think a good place to just start with what we're building is with the requirements. Like, we have been in the business of sourcing equipment for a really long time. And one of our side quests, I would say it's one side quest, but really multiple side quests that we've had have just been to help our friends and former colleagues find any manner of machine that they can even imagine. So I cannot tell you how many times I will open up my link in messengers and I've got people asking for a certain type of measuring tool or a CT scanner. I get emails about hot crimping for certain types of motor manufacturing. It can be really hard if you were just trying to build a marketplace like we were, to always just have the thing that our customers were asking for.
And so we would go out and try to first understand enough about what do these customers need, like, what is it they're actually asking for? Try to figure out, okay, who the hell in the world sells this one obscure machine that these companies are asking for? And then to go out and get quotes like, what are some actual machines that we can put in front of these companies?
And for the most part, we did a lot of this work uncompensated, just for good karma and behind the scenes. Blake, you, myself and Greg have been building up our own internal suite of AI platforms to help us respond to all of these requests that we get. And that's when I started to really get excited about what the possibilities were here. I knew that if we were putting in this much work to build an AI platform to serve our own needs, that anybody else in the same position would have to be doing the same thing. And while I'd love to believe everybody got all the time in the day to build up, cook up special Claude projects, most people just don't. And so I think that's really where we started to see this really interesting opportunity for an application layer of procurement for machines that's informed by the requirements from a customer and on the other side, matches with the suppliers and the machines that match with those requests.
There's just so much that can be done today with AI capabilities and matching those requirements with machines out there and then even deploying agents to go and retrieve quotes and proposals on your behalf so that's the thing that I'm really excited about. And if I had to describe Albus, our new product, I would describe it in this way, that it is the most highly capable and technical.
I'll call it like procurement manager, technical procurement person that you could add to your team. And I think that people are responding pretty positively to it.
[00:24:28] Speaker B: And I think for anyone listening too, we'll include a link in the description so you can obviously join that link which will get you on the wait list to be one of the first as we continue to roll it out to folks that are using it. Greg, I'm curious to hear your take on how you're seeing Diagon deploy AI in different ways, some of the challenges you're running into, and then also you know, what you're excited about from an Albus perspective, this new product we're cooking up.
[00:24:54] Speaker C: Yeah, I think maybe I'll zoom in a little bit and just describe like two use cases I think that you could imagine pretty easily. When I think back, sometimes I would spend a week, probably a week just taking PDF, Excel documents and PowerPoints and then putting them into a centralized either spreadsheet and or PowerPoint. I might spend a full week or maybe more just calling like normalizing it and making it presentable to someone. Like I think most people, if they've used AI, it's amazing at this kind of thing, like it's not perfect. So like I wouldn't just zip it in and then send it to an executive, but it could probably help me save four out of five of those days. And then I spend the final day curating it and double checking the numbers to make sure they're accurate. But I don't have to pour through an email that a supplier sent me or a text message. I can like consolidate it all together and it will normalize it, it will make it directly comparable. And again, you may have to double check it, but it's very good at taking unstructured data and making it structured like that kind of application is extremely useful in this, in this context. The second one that comes to mind is this agentic outreach. I watched a webinar from one of, I think it was anthropic this morning, where they called it asynchronous agents, meaning they're doing work for you without you. And then you can, you know, check in with them, you can update their prompts, you can kind of nudge them in certain directions, but they're doing things for you. There's two things that the application for that come to mind. It's one is, you know, sometimes we would only assess three suppliers because I didn't have time to reach out to 10, which is like from a commercial standpoint it's not good. Like There could be 10 viable options but like you as like an individual is managing five equipment purchases at the time. Like you don't have time to reach out to 50 suppliers but agents do. And so like your agents could go assess, like if there's really 10 super capable suppliers they could go out and assess them and make sure maybe they all can't do it, but maybe there's one in there that you missed and you didn't realize because they have a terrible website but you find out from a spec they sent the agent that they could be a fit for the project and maybe their product is 20% cheaper or 10% cheaper. And so that just saves the GSN time. But I think why the business will care is it could get much cheaper, much more viable options. And I think that never seemed viable before but I think we're seeing use cases for that to be like very good. And I'm just super excited because then honestly people worry about at least this job being automated away. But like we all hated that part. These parts of our jobs and it's like the strategic there is still, you know, some like personal negotiation, there's other relationship management and other things that the procurement or the engineering teams can spend their time on but they hate this like emailing a dozen suppliers and having to sit through these boring presentations like all to find out like they can't even build the thing you want. So yeah, I think these two applications just unstructured to structured data is amazing. It can save me a week, a minimum. And then these agents, I think there's a lot of potential and we're just kind of scratching the surface of, of both of them.
[00:27:59] Speaker B: I'm with you on the augmenting piece. I think from a go to market and marketing perspective alone, there's plenty of ways to augment some of those pedantic tasks or start to think differently about your type of work and the way that you're able to run concurrent tasks all at once. Whether it's from a marketing perspective or what Albus will be doing as people are able to start run concurrent searches of different kinds. These are all impactful, big things. And I also think from a job perspective we're still seeing where the dust is going to settle in that Microsoft put out an interesting report on this just about a week or two ago from their research team and it was all around different types of jobs and which jobs will be impacted, which jobs will be potentially augmented, and then also which jobs are just hard to replace. And funny enough for us in the manufacturing space and we work with a lot of battery companies, obviously material sciences is quite hard to replace. Material sciences is still not just a science, but an art form. And it's something that AI is going to take a while to learn some of these things.
I do think though, it will be important as people are essentially almost learning this form of alchemy and applying it to their different types of industries. Who knows how to apply the alchemy will be the winners. And I think there's people both diagon in the manufacturing and manufacturing equipment space and other folks across other industries who are set up for success in that way in a really interesting way.
[00:29:27] Speaker A: So I agree with that. Yeah, I also would love to go through that list. At some point I was taking a look at the list and it was, it felt kind of haphazard, like the types of jobs that were most at risk and the ones that weren't. But I think the thing that most of my founder friends are talking about these days is like how they're going to apply and use the AI capabilities at their disposal to disrupt a function that they've been in or an industry that they've been working in, or usually some cross section of the two. We can share a copy of this list, maybe in the, in the follow up as well. But I would say that most founders in this community are looking at that list as a challenge to be accepted on whether they can actually build something that's going to help, you know, just change the way people do those jobs.
[00:30:21] Speaker B: I love it. All right, we're going to move to my favorite part, which is hot takes. And for those of you who listen to a few episodes, you know that hot takes started because Greg in general just has hot takes.
I don't know if it's because it is hot, quite literally in the larger San Carlos area or if it's just because Greg is, you know, monitoring the situation on X perpetually. And so, you know, Will and Greg, I'm going to go to you in the briefest of comments. What is your hot take of the week that you feel like sharing right now? And I'm happy to also share too.
[00:30:54] Speaker C: My hot take is that the economy is going to catch fire in Q4 and that we're going to see a 5 or 6% GDP print and the hottest take, which feels very risky is we're going to see maybe close to a double digit gdp print in 2026 at some point. I do think there's going to be an AI bust at some point, but right before the bust there's like it kind of overheats and I'm going to speculate that there's going to be an overheating, that there's going to be an acceleration and there's going to be a real crescendo maybe when all these people start spending money. But we're going to see a hot Q4 and a double digit GDPR print in 2026.
[00:31:34] Speaker B: I love it. So I guess we'll have to drop your Robinhood account link later, Greg, so that we can all monitor what it is you're doing as you monitor the situation will hit me. What are you thinking about? What's your hot take?
[00:31:47] Speaker A: My hot take is that we're going to see a reanimation of the startup ecosystem and it's probably not going to be this year. I'm thinking in 2026 though, there's going to just be an entire crop of companies that are building on the foundation of the ecosystem that companies like Meta, Anthropic, OpenAI, deepseek, they're all building and I think that's going to be a really exciting moment. I think venture backed startup activity has been slowing down for the last few years and I think that that's it's also in for a U turn.
[00:32:23] Speaker B: All right, my hot take. This one is coming from my roots as a farm kid in the Central Valley. I grew up on a farm in Modesto. For those of you that ever need a pub trivia partner who needs to know about cows, I am your guy. I think we talk about AI and we say that jobs like mechanics, plumbers, et cetera are completely safe from AI. And I would agree with most of that. But I think what we're starting to see now is multimodal uses of AI become more prevalent, meaning pictures, videos, et cetera. What I'm finding interesting, and this is purely anecdotal, from a significant amount of friends and family getting the first pass of their queries done by posting a video, going into video mode of ChatGPT pictures of their engine of the water hot water heater that's having issues and it's kind of giving the first pass of well, have you tried resetting it? Have you tried turning it on and off? Have you tried doing this? Checking the pilot light? And so my hot take is actually that I think we might be heading towards some of those types of roles being impacted or potentially augmented, depending how you look at it, in a far more impactful way than I think we all are realizing. And so I think that's my, my hot take. It's also an optimistic take. I think if you are the person, the plumber, the mechanic who has the alchemy ingredients to go ahead and augment your job, you could diagnose Greg's air conditioner in San Carlos in a far faster amount of time than you would have before. And so I think there's a lot of those kinds of interesting insights that are important to call out.
Not as exciting as Greg's speculation on the public market. But, you know, I thought you were.
[00:34:02] Speaker A: Going to tell us about like, AI powered robots that are going to milk cows or something.
[00:34:07] Speaker B: Oh, those have already been in market for a while. And if you join our next episode of Movers and Makers, I'll be going deep into the ups and downs of the utterly interesting business of milking cows.
Gentlemen, is there anything else that we want to cover or dive into for today's episode or anything else? Any parting thoughts?
[00:34:30] Speaker C: This is not financial advice. But take this is just my hot take.
[00:34:33] Speaker B: There will be disclaimers in the show. Notes left and right for everyone.
Folks, thank you so much for listening to today's podcast on behalf of myself, Blake Menezes, Greg Smythe and Will Drury. If you are listening for the first time, be sure to check out some of our past episodes. Drop a comment, send us a message and subscribe on Spotify, Apple Music and YouTube. Thank you so much, gentlemen. Thanks for all your takes. Appreciate it.
[00:34:56] Speaker A: Thanks, Mike, Sam.
