Wharton’s AI Report, Tech Field Day Vibes & Data Centers in Space | TSG Ep. 959

Hey everyone, adoption gains in Gen ai. Did we need Wharton to tell us that you're watching Textron Gang. Hi everyone.

Happy Tuesday. It's great to have you on here. You know, I'm, I'm still recovering from the weekend, but, uh, we're already into Tuesday and before you know it, it'll be Wednesday and Thursday, such as, such as life in the Big City, as they say.

We've got a great show line up for you too. We got some great gang members. Let me introduce you to them.

We've got my friend Steven fst, also a friend, JP Morgenthal, a friend and only occasional gang member, but we are lucky to have him with us today. One and only Dave Nicholson. Uh, Mitch Ashley, of course, Mike Ard, and myself, Alan Schl.

So Mike, as I teased in the opening, it seems gen Gen AI adoption is gaining ground. If Wharton says it, it has to be true. It may come as a surprise, but there's a little bit of a debate in the academic community who would've thought, we've got Wharton saying that, well, yeah, people are starting to see ROI from these investments, and they're gonna invest more.

And it's kinda interesting 'cause it's juxtaposition against an MIT study that came out a while back and was basically saying that 90% of these projects are more fail and cast a much more, uh, Dora Outlook, shall we say. So Dave, let's start with you, but what's your take on these things? Are these reports really polar opposites, or are they kind of maybe, you know, different parts of the equation?

No, I don't think they're polar opposites. I think if you dig into the, of course, there's gonna be rivalry between, uh, MIT and Wharton. Uh, my joke is always that, uh, there's a reason why the Constitution was, uh, was signed in Philadelphia and not Boston.

Um, I don't know what that reason is, but I, I, I, I chalk it up to superiority. Um, now if you dig into the MIT, the MIT study, it's not as bleak as the headline. Um, but I think, uh, we, and I say we, because, uh, I'm actually an instructor in, uh, the, uh, senior executive program in AI at Wharton, and also the c the, uh, CTO accreditation program.

Um, my partner in crime in the AI program is Persona, Sonny Tomba, professor Tomba, and he's one of the co-authors of the, uh, of the study. Um, I think that Wharton's methodology was more sound, frankly. Yes.

Am I biased? Yeah, I am. Um, but I think it's, it's a more accurate reflection of what I see with our students in the program, and that is, um, something that maybe the MIT study glossed over a bit, and that is this differentiation between good old fashioned ml, uh, you know, neural networks, deep learning, and the dawn of not only natural pro natural language processing, but specifically generative AI moving forward.

So we took a look at those variables independently, and we're still seeing a lot of good old fashioned machine learning, uh, under the AI category, but the rise of generative ai. Absolutely. So the headline is three quarters of enterprises are absolutely underway.

The vast majority of, uh, executives are personally using these tools. Uh, and by the way, the number one tool by far chat, GPT, open ai, uh, to the tune of somewhere in the neighborhood of 70%, despite the fact that, you know, the Microsofts and, uh, Googles of the world are giving away their own tools at first. So, uh, so really interesting, but no, I wouldn't say polar opposites, but I, but, but, uh, definitely, um, uh, I think a more realistic and, uh, and a and a and an optimistic view.

I'm trying to figure out, um, did they ask different questions? Honestly? Because as I looked into this thing, I was like, Hey, well, Wharton's asking about how their investments in gen AI are, and if they're still optimistic about it, and MIT was basically looking at the previous investments and maybe more of a trailing indicator and earlier in the cycle where people are saying, yeah, well, we invested, but we didn't get the return on it.

But it doesn't mean we don't continue to believe in it. But I don't know, Steven, what's your take? Yeah, I think that it's different questions, and for me, um, I would kind of put my, uh, honestly, I would stand behind both of these studies.

I would say 95% of the projects have failed. And I would say, um, what's the number here? Uh, you know, so many percentage of these, uh, attempts to use artificial intelligence, 82% have succeeded.

I don't see that those are different points being made. And this really jives with what we've seen, uh, anecdotally from our tech field day events, including AI Field Day last week, which we're gonna talk about here in a minute. But, um, that basically there's a shadow AI that's happening just like word processing and spreadsheets came into the business, just like the internet hit so hard, just like so many things in the past, so many technical technological revolutions in the past.

And just like those, you know, I mean, I think that if MIT had studied, I don't know, e-commerce in 1997, they would've said 95% of e-commerce projects fail. Yeah. Um, I think that's probably true because it's early going and it, with ai, it was, it has been early going.

And frankly, what I think is that these top down sort of, uh, traditional IT projects tend to fail, especially early on in the adoption of technologies like cloud or e-commerce or whatever. Um, and then later, uh, a, the IT crowd kind of comes along and kind of figures out the right way to do it. And so now instead of 95% of, I don't know, cloud adoptions failing, it's gonna be 95% succeeding.

And I think we're gonna see that as well with generative ai. So, Mike, I, I, I think there's a few factors here that we need to acknowledge. Number one is the rapid acceleration adoption and maturation of ai, understanding that we're still at the beginning of the beginning here.

We're not even anywhere near past that. And so it's going to continue to mature and, and accelerate. Number two, it depends who you ask as the follow up, uh, article.

In, in our, uh, set in, in this particular, uh, section of today's show, there's this divide between CEOs, CIOs is divide between the business people and the technical folk, right? One of the hardest things to put your thumb on in technology that I've learned over the years is ROI. There's metrics, you know, stats, metrics and lies and damn lies or whatever.

So it's very easy to say, oh, it had no ROI, right? But I think when you take into account timeframes, when you take into account, are you talking to a business exec versus a tech exec? And then here's the other thing, and it goes to what Steven said.

I agree with them. Most of these technologies that I've seen in my 30 plus years don't start top down. They need air cover from the top.

But the fact is that successful implementation start off very much as tiny bubbles with the larger enterprise, uh, medium and those tiny bubbles. One in, you know, small teams, individuals to small teams, to bigger teams, to, you know, division-wide, company-wide. That's the progression of, and, and at each step it gets a little better, a little more mature, a little more scalable.

And I think that's where we are here. I think one of the biggest problems we're hearing, or you gotta apply to the AI doomsayers who are, you know, saying it's not happening fast enough. The money invested doesn't, you know, correlate to the reward is you can't make wine before it's time.

This thing is percolating through and and maturing before our eyes. And so I, I think of MIT, or dare I say Harvard or Yale did a, uh, uh, a study six months from now, nine months from now, it's gonna be even rosier. You know, Alan, I think as I jump into this, by the way, Dave, I think with the Wharton team did a fantastic job.

So hats off to you. It's a really well done study. I think, I think what's interesting that pops out to me in the data is the fact that we're shifting from experimentation to now let's budget, let's put some ROI or put some KPIs or some kind of performance metrics.

Doesn't always have to be ROI. Um, and then that hasn't dampened yet. So we, we haven't hit the trough of disillusion yet, if you will, and know, you know, we're all, we all talk about the AI projects that fail, but now, you know, real money and expectations are being put behind ai.

So we are moving up this maturity curve with ai. And to your point, Dave, um, you have to think, you have to look at machine learning as well as expert systems, neur networks as well, as well as generative ai. 'cause it's all part of that picture, right?

And oftentimes AI becomes the label for generative ai, and which isn't really an accurate term. So there's some great data about accountability is the lenss, the impact is rising performance justify, uh, investments. Lots of good things in here.

I'd definitely recommend people read it. Jp I'd love to get your opinions on what's going on here with this whole survey. But one of the things that I do observe is that there's a disconnect between the C levels and the middle managers.

And I also notice that just because I'm more productive, it doesn't seem to me that that equals more revenue or more net income for companies yet because there's a disconnect between, well, uh, I had an easier day, but it doesn't mean there was more customers to buy something. So, I mean, there's a lot to unpack around this. First thing when I read the Wharton study that came to mind is it's very, uh, individual productivity oriented.

And I think that's a key point. Uh, I read it as almost as, you know, this is an, an additive productivity tool, not unlike when we got office and, you know, um, Lotus years ago, right? The, the boom that occurred when people had electronic productivity enhancement tools, and now the next productivity enhancement tool is these, uh, LLMs, we'll call 'em.

But you know, really what they are is gen AI chat, you know, inha, you know, chat tools, right? So people are using, I read the Wharton study very personal, like people, how is it affecting you? How is it making your life easier?

How are you using the tool? I don't see it a lot as enterprise ag agentic ai. I don't see the representation of ag agentic AI whatsoever in this study, which tells me that it's easy to avoid pain.

It's easy to avoid failure when you are not moving towards, um, an ai, uh, autonomously working and trying to achieve a goal. And then having that goal, having to integrate, uh, and automate some of the most complex systems that run our businesses as well as implement new processes. I think a little bit of that was in the MIT study and captured, and that's why we saw that there was, you know, more, uh, downside to, you know, to the experimentation.

But I think it's great. Uh, I also think that one thing that didn't come through clearly, and I, I may be wrong, but what I saw was that a lot of these tools are being used through, uh, or being adopted through the use of other tools that have incorporated AI into them. And that's, and that's to be expected.

Now, to your point about ROI, first of all, increase in productivity has always led in the industry economically to, uh, I, I, uh, improved ROI and and improved economics just has, um, do more with less. Secondly, the, uh, the ROI comes from the fact that individuals wearing multiple hats can now, um, achieve more, uh, without having to increase the human labor, uh, pool. It's true, right?

It's just, it's just a factor that's real is and is that a single person using these tools can get more done. They, and, and they can, it'll write a document for you. So instead of four hours of writing, you are typing a prompt and then moving on to the next task while this thing is writing your document and your, your PowerPoint.

That is what I see between both of these studies and also as a prognosis for where we are in the industry and it's good, but this is really focused on productivity tooling and that more and more people are starting to use the tools, uh, that is available to them. And by the way, I think it noted that it's expensive. It is expensive to still bring this into your organization.

This isn't like turning on office or, or, or Microsoft office, you know, circa, uh, 1995, uh, where you got the whole office kit and everything came down and you paid two 70, uh, $270 a person a a year to Microsoft. This is unbounded, this is unlimited. The upside costs, you know, can become exponential, uh, depending upon consumption.

Yeah, I can weigh in on the, uh, ROI question if, if, if, if you'd like from, from from the study. Um, something that's important to understand about how critical it is that we're seeing individuals use these tools. It's the individuals that are using these tools and, uh, as we move higher up the stack, we're seeing senior executives become familiar with these tools.

Why is that important? Um, because if you've never experienced these tools, you can't imagine what the problems might be that you can solve with these tools. You know, the MBA admonition of first decide what the problem is you're seeking to solve before you talk about technology.

That remains true. However, if I were to gift each of you 10,000 acres of land and ask you, what are you gonna do with this land? And then said, wait a minute before you answer that question, since you've only used a shovel before, I'm gonna show you a short video clip of a cat, caterpillar D nine Earth mover.

You don't need to know if this giant smoke belching yellow thing is a beast or a machine, it doesn't matter. Your imagination has now just been expanded tenfold when you see this thing plowing a road through a mountain. And that's the key enabler here for unlocking real ROI moving forward, is this idea that executives are having their imaginations expanded for what the possibilities are.

Um, the other thing on ROI that's interesting is this question of to whom will the benefits accrue? If I become 300% more productive, uh, and I'm an employee for a of a of a big company, does that mean that I get an extra Friday off every other week? Uh, does it mean that I get a 25% pay increase?

Uh, am I splitting the accrued benefits with my employer? Or are we in an era where the expectation is you just need to be 300% more productive and, uh, you're gonna live a year longer, but God, it's gonna be great for shareholder value. Um, you know, very, very real question.

You know, Dave, to that point, you know, I'm reminded of that meme of all these CEOs C-level people saying, what do I want ai, when do I want it? Now? Why do I want it?

I'm not sure, but I want it. Right? And, and, and we are, we do have a little of that going on.

I saw another study, and I, I'm trying to remember, it was Gartner or someone else, but something like 60% of CIOs are asking for greater budgets for ai, not because they actually have definitive plans to spend that money or, or, you know, clearly enunciate what those plans are, but their board and their CEOs telling them they gotta spend more on ai, right? And you said They're bored, like, yeah, I'm bored. No, I get, yeah, they're bored.

Yes. No. Yeah.

Yes. Here though is they're director's boards. And so, So lemme lemme argue the opposite of Dave there for one second, though.

I agree that it is fabulous that CEOs are playing around with these AI tools, but I also think that they're discovering the limitations of these tools, and that's a good thing, right? That bulldozer that you just described does not fly. And right now, a lot of the expectations that a lot of the C-level execs had was that there was gonna be some magical thing happening here.

And maybe now they're gonna realize, well, you know, this is an improvement, but it's not magic That I, I have to tell you, I have to tell you huge point that I have to make with my students who are C-level executives. Um, when they tell me, I don't need to know the technology, I'm like, okay, you at least need to understand the difference between something that was generated, in other words, made up, versus something that was retrieved. Well, what do you mean by that important differentiation to your, to your point, Mike l understanding the limitations of these tools.

It's, it's important. Well, I think we have to recognize too, you know, a contrast to innovations. This isn't blockchain.

This isn't something that only a few can get access to and understand how to use it and leverage it. This is a technology that anywhere anybody in the organization can use both in, in work and outside of work. And, uh, we have executives adopting them just like they were early adopters of blackberries, right?

They saw the value of having a, a device like that, and it helped, uh, the adoption of those kinds of devices in, in the organization. I think that's what we're happening here. And to your point, it's shaping in their mind, at least.

Now, Alan, they may not, they may not be able to answer, what do I want it for? But I think I might see what I want it for. I'm starting to see where this can benefit me versus the technology guys going in and say, here's why we need this budget.

And like, I have no idea what you're talking about, but last time, okay, you kind of delivered. So I'll, I'll put, I'll put another bet on you in this generative AI thing, or blockchain thing or whatever. Absolutely.

Guys, we're 20 minutes into this segment. We gotta jump onto the next one. Let's take a quick break here in the gang.

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And as Alan said, we're gonna have a little chat about a field day event that Steven and his team hosted with a bunch of AI vendors and some AI experts, and they had some interesting conversations, which will probably continue along some of the themes we've already discussed. But Steven, what are the folks saying? What are the people saying?

Yeah, I think that's the interesting thing always when we go to these field day events, not, you know, I mean, the, the presentations are great. It's great to learn from, you know, new companies and so on, but sort of that back channel, uh, is the most fun, especially for, uh, listeners to the Textron gang, because that's basically what you're kind of eavesdropping on here with us every day. Um, so what did I hear from the delegates, from the presenters, from the guests and so on?

Well, you know, here's a, a few takeaways. Um, so first off, um, there's a very strong trend and, and really preference, uh, among the delegates, among the, the companies that are really working on, on enterprise products that are gonna go somewhere, uh, to build special purpose tools, not general purpose tools. In other words, it's not about taking, you know, making chat GPT part of your workflow.

It's about, um, and, and, and that may be useful, and it may be productive on an individual basis, like we were talking about in the previous segment. But, um, when it comes to building sort of, uh, you know, the next billion dollar enterprise software company, when it comes to building out AI in a, uh, a specific vertical, you're gonna need special purpose software. Um, that was very clearly the message of Articulate, which is one of the companies that presented.

Uh, but it really kind of rang through the whole event. There was a lot of thought of, you know, okay, you know, chatbots are great, LLMs are great, but they're just a tool. They're just a user interface.

They're just a way to interfa interact with, with software. Um, what next? What else are we gonna do?

And, and we saw some pretty cool stuff around that. So that's the first point. The second point, um, and, and this I think kind of goes hand in hand there, there's a lot of interest from end users of working with a big trusted partners that can deliver the whole stack.

In other words, they're not, they realize, I think that buying some GPUs, or even buying some GPU servers or even a, even a functional cluster is not gonna get you there. You need a partner that's gonna bring everything, you know, from hardware and software or, you know, as a service infrastructure all the way up the stack and partner with you on building one of those kind of bespoke AI environments that serves the needs of your business. And that's where, you know, I think some of the big incumbents, like companies like HPE, which presented actually show themselves pretty well, because essentially companies are already used to working with them.

They're used to picking up the phone and saying, you know, Hey, IBM hey Oracle, hey, HBE, hey Dell, uh, I need a a solution. Can you get me a solution? Having those companies come in, not with their own in-house stuff completely, but with their in-house stuff as well as, um, part products that they vouch for, partners that they vouch for.

And, and so that kind of came through. And then finally what we were just talking about here absolutely rang through shadow ai. It really is like the old days of cloud computing when, you know, people were putting their card down, signing up for an AWS account and starting to deploy things because they couldn't wait for I it to, to bring cloud into the enterprise.

And they saw the benefits of it. It, it's the same as your blackberries, it's the same as your VisiCalc on the PCs and so on. Essentially, people are bringing this stuff in, they're doing the thing, and it's up to it to catch up.

And that kind of came through, for example, when Haiku talked about the analogy between SaaS applications and, uh, these new AI applications. So just like in the SaaS world where, um, you know, this department would sign up for, you know, Trello and this department would sign up for Slack, and this other department would sign up for Monday because it met their needs, businesses are now starting to say, whoa, I've gotta get my my hands around this data. I've gotta start figuring out what's being used, where it's being used, what corporate data is out there, and just like in that space that it's gonna be challenging because there's so many providers.

It's like that with, uh, ai. And so it is very likely, uh, that every company is using open AI and Anthropic and, you know, Gemini and Claude and all of these copilots. It's very likely that they're all in use, whether companies know it or not.

And so they need to start thinking about their data. So those were sort of the things the delegates were talking about that sort of percolated through the, uh, AI field event. Do you think, going to that Blackberry point, and I'm going back in time myself, but I distinctly remember like when they was first arrived, the pace of things started to pick up, but it took a while for the rest of the organization to kind of adapt to that.

And a lot of folks were like stressed because suddenly they were getting, you know, 10 times as much email and they were suddenly being asked to answer and respond to things. And I don't know, jp, you were there for those days. Is this, is this very similar?

Is this a cultural issue as much as it is a technical issue? It's, I, it, it, it has all the makings to be, um, a cultural issue. But I think ultimately it's gone beyond that because of the nature of what these things do, the role that the they perform and the capabilities of the, of this technology is beyond what we, you know, would typically assume associated with a, a cultural shift, right?

This is now, this is transformative. This is transformative on society, it's transformative on enterprises. It's, it's, you, you, you can't ignore it.

You, you know, we, we look at the laggards and, and what's the, at least the people I've spoken to, everybody has the same comment of laggards. You know, they're doomed. They're, you know, they're, they don't have a prayer if you're not on board, if you don't get this, if you don't do make a change.

Now, if you don't get on board with this, you know, there, there's no, you, you, you won't, won't have a job. You won't exist or business will be gone. It'll be taken over by competitors.

It's a very, very negative sentiment to anybody who's not participating. That was a great argument to get people to be the first over the top of, uh, world War I trenches also mm-hmm. Point in time For the mustard guests.

Yeah. Fear of, fear of missing out. You know, I, I think Tech Field Day is a great pushback, frankly, on the meme that we discussed earlier, this idea that business leaders don't know why.

They know exactly why, why they wanna make money, they wanna save money. It's the how they don't understand. And, and you know, the, the, the old saying that those who know how will always report to those who know why, and those who know why, we'll always make more than those who know how.

Um, increasingly, and kind of to jps point about, you know, how this moves forward culturally, um, you've gotta know, you've gotta know a bit of how, in addition to the why, otherwise you're gonna be sitting there just going, yeah, I know why we wanna do it. We wanna make money, we wanna save money, but, but I have no idea how to get this done. Those are the kinds of answers that, um, that, that get delved into at something like a tech field day event.

You have to get into a little bit of the weeds to, uh, to, to figure out how to extract value from these things. You know, one of the things I wanted to comment too is on, Steven, on your point about companies are starting to specialize in their use of ai. You see this in multiple areas.

You see it in software development where AI has become this generic tool that you use to generate code or write things with or do whatever, and it, and companies went through the chatbot phase, which is kind of low hanging fruit of let's just put a chat bot in our app, and now we've got ai. Now what do we do? Right?

Um, but you see companies, uh, I use the software industry, for example. Some companies recognize we're not a company that's gonna build agents. We're gonna a company who's got data.

And so we're gonna work on how people can access that data through our infrastructure, or we're, we're not trying to solve every software development problem. We're gonna focus on modernizing, particularly around Java, that kind of thing. You see the models as well as tools coming out.

Uh, so you, you, you start to see the formation of what the strategies are or what companies, how they're planning to use. It's, right, to your point, Dave, about, so how is this, how are we gonna use this? Now, these are tech companies, tech executives, they have to know what they wanna do too.

And I, I think they do have to know Some of the how as well at the risk of oversimplification, man, I'll throw this down for the hell of it, but, so just because I can create 10 marketing campaigns faster, it does not necessarily follow that there's more buyers out there that consumes that marketing campaign intent, and we'll actually go purchase something as a result. So, um, where is that kinda benefit? I mean, uh, does it just mean I'm gonna use fewer marketing people to create the campaigns for the existing customers, but am I really expanding the overall market because I can create marketing campaigns faster?

I don't know. I think the, the difference there is, um, that it's not about attracting 10 more buyers. It's about being 10 more, 10 times more efficient.

At least that's the pitch that I get from these. You know, you talked about marketing campaigns. I am pitched by companies using AI for marketing campaigns all the time, and their pitch isn't, um, you know, we're going to send out 10 times more.

Their pitch is, we're gonna send out 10 times better, and you're gonna get 10 times the response rate. And, uh, 10 times, the more you know, more interested customers, you're gonna rise above the spam filters. You know, people are gonna feel like you're a real partner, that sort of thing.

And, and I wish that AI would do that. Um, I actually am hopeful that some of these special purpose AI applications, um, can deliver that kind of personalization. And, you know, and, and it's not, you know, in a cynical way, you know, I wish that I didn't get irrelevant marketing pitches on a daily basis.

Um, and, and I think most of us do. Um, and, and it's the same with pretty much everything else. It's not about, uh, it shouldn't be about higher volume, it should be about higher quality.

So I, there's part of the equation that you're missing, Dave, hit it. It's not just about making much more money, it's about saving much more money. And, you know, when we talk about AI marketing, make no mistake, a huge pun.

Part of that is cutting the marketing team. I think marketing teams have borne a disproportionate brunt of job losses as a result of ai. But here's a funny thing I know from being CEO of my own company for a long time, and being a co-founder of many companies, I don't think anyone picks a market that is so small that I, I, I am proud enough to think that my marketing reaches the whole market.

As a matter of fact, myself and every other executive I've ever known, always have the feeling that this market is so g*****n big, and I can't, I can't get, I can't reach most of these people. Half of these people don't know who I am. If I, they knew who I were, they would, they would buy from me.

That's what startup entrepreneurs, that's what most companies think. How do I reach the parts of the market I'm not reaching now? And, and I think that's the promise of ai.

I could, I could do more, faster, cheaper with less people, especially when it comes to marketing, right? It's, I think we've barely scratched the surface surface. To Steven's point about, you know, having specialized marketing, ai, you know, service companies, product companies, SaaS company, whatever you wanna call it, that's gonna finally help me reach all those people in my market who don't know about me.

It's, there is definitely a scale factor that is a, that is part of this, right? AI not only will help you get your message out there, it allows you to expand the audience and the audience types, um, that you're preaching and, and that you're sharing the message with. Um, typically why do you want to keep a particular campaign at a certain size so it's manageable so you can a react to, am I getting a good response from this campaign, or is it not going the way I want?

Secondarily, if it is a successful campaign, how do I respond to it? I don't want stuff just coming in and, and falling off the end of the cliff because there's nobody to catch it, right? Um, which is the other half of the campaign.

Ai, especially around a lot of these AI based marketing tools are there, uh, to assist on the front end and the back end. On the front end. It's, you can now go after finance and high tech at the same time.

And because we're gonna be there as your catcher so that anything that comes in that's good, we are gonna be able to raise it to your attention and so that you can act on it quickly and we'll route it for you to the individuals who can handle it, right? That's, I i, and you don't need to now go higher in order to meet that scale demand. You can leverage the resources you have in-house.

That, that is the way I read marketing gain with ai. Alright, Steven, I wanna come back though to your event. Um, what was the mood?

I mean, were people enthusiastic or were they kind of skeptical, or what was the vibe? The vibe was, uh, enthusiastic a hundred percent. And, and, and, and not the sort of, uh, AI skepticism that, um, we hear a lot here as or on, uh, some of the other, at some of the other field day events.

Uh, this crew was very enthusiastic because they were, they had already moved past a lot of the things that get us all wrapped up in ourselves. Um, they, they understand that, you know, uh, stealing volumes of data to train your model stinks. They understand that, you know, building giant data centers that suck down power and water stinks.

Um, but that's not what they're talking about. And so they've moved beyond that a lot. And, and this is exemplified by the way, in the episode of the Tech Field Day podcast from Tuesday last week, where I had a couple of folks from AI Field Day on, and it was an entirely different vibe, so I really glad you brought that up.

The reason is that these guys are actually doing this work. And so you have people like Calvin Hendricks Parker, who is building agentic AI applications and using AI coding as a real, not just a pro, like a, a, a professional software developer, but a, you know, a one, you know, a plus professional software developer who's using all these coding tools in a practical way, and it's really helping accelerate his work. You, you have people like, like I mentioned, um, you know, articulate and some of these others that are, are building applications that run, um, you know, Ryan Booth, one of the delegates is, um, building AI applications and actually kind of working on some pretty exciting stuff in the, in the background.

These are not, you know, let's build super intelligent AI Elon Musk fantasies. These are like, I need an application that helps me do, I don't know, laminar flow calculations for, you know, aeronautics or I need something that's gonna help, um, you know, make, manage my, you know, greenhouse vegetable production and, you know, kind of, I don't wanna say boring things, but it ain't marketing automation. It's, it's, let's do something productive for society.

And so that's why they're enthusiastic about this stuff, because I think that they are seeing that there are productive uses for this technology. And on that point, I'll just also add too, that we're gonna be launching, um, a brand new podcast here on Textron Group. ai.

We recorded it live on Thursday, and it's actually gonna be published tomorrow, Wednesday, the first episode of that. And, um, we're calling it utilizing AI because the whole point of it is rather than just getting wrapped around the axle again and again and again talking about hallucinations and chatbots, and, you know, let's actually talk about making practical use of this technology and where it can be used to help people, to help businesses to do things, not just, oh my gosh, it's lying about politics. Cool, guys, we gotta move on to our next segment.

We're, we're just running over time, all over the place today. Let's come back here and talk about space, the final frontier you're watching, Textron Gang, Discover Textron Group, the epicenter of tech innovation. We are your go-to for reaching IT, leaders and practitioners worldwide.

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Contact us today and tell your story to the world in the most powerful way with Textron Group. Hey, folks, we're back. And sometimes we just do things because we can, but maybe we're not quite sure what their purpose is just yet, but Cruso and Star Cloud are gonna build a data center in space with some GPUs, and they're gonna run some software up there.

I'm not entirely sure exactly what, but JP is this, like one of these John F. Kennedy Space mission programs. We're just doing this because it's hard and we wanna see if we can do it.

I, I think it's an extension of, of stuff we're already doing. I mean, we sunk a data center to the bottom of the ocean, uh, and we leveraged, uh, uh, renewable energy from the waves to feed power to that data center, uh, which, you know, is an important factor, you know, how do you, you know, getting power, obviously to the data center is gonna be your biggest question. Renewable makes the most sense.

So, um, the, the answer's the same, right? A, I have why, why the bottom of the ocean? Uh, it's cold.

I got, so I have, uh, I'm able to keep the temperature down from all that heat that I'm emitting from the processors. The power is renewable. Um, what's the one challenge?

Well, if something breaks, how do we fix it? And so you have the same, i I think issues, uh, probably more expensive in space, but with space, you get a, uh, you know, a continuous renewable stream with solar, you can follow the sun. Uh, so you have 24 hours of solar energy that's feeding your data center.

You have the cool, the, this temperature control from being in a vacuum space, uh, which is, is going to relieve, you know, and provide, uh, optimum, uh, performance, you know, for the machines that are running in there. And the same thing happens. What do you do when, when it breaks, right?

And now I'm setting up people, uh, to do maintenance in a, uh, SpaceX craft or something like that, unless, you know, the data center carries SpaceX technology and it lands itself back on earth to get fixed and then takes off again, which is great. I mean, we know we can do it. I, I, I go out in front of my house at least once every other week, and I watch these things take off and, and the, um, and the fuel pods come back to Earth by themselves.

They, you know, navigate themselves back down to earth for renewable, uh, approach, you know, use. So, um, not, not stupid in any way, shape or form. Um, looking at a way of saying, how do we get away from, how do we do this thing that's gonna to eat the entire Earth AI without consuming all of our natural resources?

Because it has, it has the momentum and the potential, uh, to, to truly, you know, eat incredible amounts of, uh, of our natural resources in order to f fulfill. And this is a way to maybe get around it. Now, I I also think you have some interesting challenges with, um, communications, right?

You're gonna see blackouts from sunspots and things like that the same way we do with, uh, uh, you know, the communications technology that, that's running from the SpaceX organization today. So, uh, it, it's gonna be a learning, uh, you know, uh, process for them. But it's not the, it's not the dumbest, you know, thought in the world.

It's not, It's not the, it's not the dumbest, hold on, hold on. It's not the dumbest, it's just the second dumbest. Well, second to putting things at the bottom of the ocean where there's salt water, there's this thing called the rocket equation.

I can tell you that a data center rack full of, uh, full of GPUs, a 64 node cluster of Nvidia GPUs, the entire thing weighs about 3000 pounds. So if we care about anything, uh, messing with the atmosphere, look up, look up what's required to put 3000 pounds in orbit. That's a single, that's a single data center rack, um, that might consume, you know, 500 kilowatts.

The good news is, yeah, closer to this, you know, out of the atmosphere, solar power, it's cool. Uh, you know, it's cool out there. I, I read this as a money laundering operation, frankly.

I really do. I really do. And jp, I think you're, you make a great, you make a great case for, you know, it's, you know, it, it makes sense.

It's like, yeah, just enough to part fools with their money. 8 trillion on AI data centers, you gotta find somewhere to sink that Money. That's exactly right.

No pun intended. Or shoot that money up. I think It's the mar the market is hit.

We'll, we'll invest in, in, I gotta tell you the truth though, when I was reading this, and excuse me, my age, excuse me for my age, but I was reminded of that movie with Bruce Stern and he had the three little robots that took care of the forest, Huey, dewy, and Louie, you remember that one was A great movie. That was a great movie. And that's what I'm thinking.

Because this way if, if, if, if you know, s**t goes to hell in a hand basket down here, we could jettison that AI data center out in the deep space. We can't get stuff that far in industry knew we taking care of it. Yeah.

Isn't a great Movie. We can't, we can't, we can't, we can't, we can't do it. It's the same reason why when people ask, well, why didn't, why didn't we just fly the space shuttle to the moon?

It's like, uh, because if you're talking about a huge amount of mass, it just, the map doesn't add up. Once we have a space elevator put together, we have this zero, we have a zero, uh, weight fiber that we could put a space elevator on, then all this stuff's gonna be, it's gonna look great. Oh, the first thing I thought of was, okay, yeah.

So you can get light for energy. Yes, you've got space for cooling, but well, You don't have space for cooling. That's just not how it works.

Vacuum, vacuum, hold On. There's this little thing called bandwidth. You know that data centers consume a lot of great Latency Here.

42 terabits per second out of A-A-W-S-I know we're not talking about that scale of it, but it seems like you're pretty limited. I it just, you know, if, and Invidia chips were cheap and we can just fly 'em into space. 'cause Yeah, we got plenty of them.

Okay, go, go for it. This seems like a really stupid idea. Wait, we talked about movies, Steven, isn't this just how Skyden gets started?

This is their first Note. Silent running was the name of that movie. Can I a different Fiction That actually has some science to it.

Um, if you read The Expanse, like literally like book one, like chapter two, they talk about the challenges of cooling in space because it's like, like cruise. This, this, this company literally says that it's minus 270 degrees in space. Do you know why they say that?

Because that's the definition of absolute zero. Mm-hmm. It's not minus 270 degrees in space.

There's nothing in space. There's no cooling. Cooling doesn't work cold.

You know, they say space is cold. I don't know, like aliens and stuff like that. Space isn't cold, space isn't anything.

You, you, it's so hard. If you read like any actual science about any like 50 years we've been sending satellites up there and we've been struggling to cool these things. It is you, you collect, the problem is you collect electricity.

You, you, you turn that into heat. What do you do with the heat? You gotta dissipate it.

Thermal Management. But it doesn't work because there's nothing to dissipate it into it. There's no medium.

So what they have to do is they have to use radiative cooling. They have to actually create infrared, um, energy and beam that into space. This is like a whole other thing.

It's, it's, it, it's not cold in space. Alright, Alright. Alright guys, we're gonna take, we'll take a thumbs down on look.

Jp, I appreciate your enthusiasm and, and you came out here like the little boy in the room with the, sorry, Jp. Sorry jp, he's a pony bear somewhere. I, I'm not acle, I'm not a a, it wasn't his idea.

Theoretical physicist. So I get it, but it sounds nice. I'm actually the common man.

This is way everyone look it. Right? Bottom of the seat, top of the space.

It's all good. But we gotta wrap up this, this one. I'm sorry guys.

Thank you for joining us. Thank you out here for watching and listening to us. We'd love to hear what do you think about data centers in space?

Um, but we've got Text Drunk TV coming up, so stay tuned for that. We'll be back tomorrow with, of course, more gang topics and great gang members to discuss them. But until then is Alan Shimel for Text Drunk Gang.

We're out.