Data Centers Face Backlash & AI PCs Struggle to Deliver | TSG Ep. 915

I don't want no effing data center in my backyard. You are watching Textron Gang. Hey everyone.

Happy Wednesday. Well, you know, when you get those long three day holidays, it takes a Tuesday to get back into things. And by today, hopefully you're back on your normal, even keel as we head into post Labor Day.

You know, it's, for me, it was always schools back in town. Summer's over. Let's get busy and let's get busy here on Textron Gang.

Today. We've got a great show and some great gang members. Let me introduce you to them.

We have Kate Scarsella. Kate welcome. I haven't seen this gentleman in a while, my friend, guy, courier guy.

Good to see you. Chris Blas, of course, joining us as always, and fresh off his Mexican vacation. They let him back in.

Mitch Ashley. Hey, Mitch, how are you? Senior York.

Of course, we're joined by the Dean of all Yankee fans, Mike Ard and of editors too. But hey, Mike, A great, it's be a tough September, man. I'm looking at the schedule and I'm like going, oh boy, It's a tough September schedule, but they're playing well.

Mm-hmm. We'll see. We, we will talk offline about it more, but, uh, Ben Rice, Go.

Blue Jays. Yep. All right.

So Mike, let, let's jump into things today. You know, it, it, it seems like we're building data centers, or we wanna build data centers, or we're pledging to build data centers, or we're hallucinating about data centers, but we're building data centers the size of Manhattan and just, you know, encompassing thermonuclear, uh, devices and hydroelectric plants and everything else. So what's the deal?

Well, let's see how that all turns out, because the folks at Data Center Watch are reporting that, well, local groups of which there is something now of 142 of them have managed to get, uh, $18 billion worth of these projects canceled. And 46 billion are further delayed. So they might be more cancellations coming down the pike, but it sure looks like to me, the local rebellion against the AI is starting to form.

So, are we gonna see more of this, Alan, and where are we gonna put these data centers? So, look, this is just such a classic, classic NIMBY case, right? We all want these data centers, all these billionaires and sovereign funds and politicians falling over themselves with checks for hundreds of billions of dollars, at least pledges of hundreds of billions of dollars.

And every, every city wants to be the new silicon data center capital of the world, whether it's west, south, southwest Texas, or, or, or Phoenix or Ohio, or, you know, the, the, the great heartland. And every, everybody wants these data centers until they're in your backyard. And that's such a typical thing.

Yeah, no, no, no. It's a great thing. Great thing.

I want all those jobs. We want all that money. It's going to ruin my view.

I don't want it. There's gonna be a nuclear, uh, power plant running it. Forget about it.

What can we do? And they go through the usual playbook. Have we done the environmental impact?

Have we, you know, looked at all the other things? Ha have we, you know, do we have lead in our pipes? I mean, they'll go, you know, and anyway, and, and, and they're valid.

I, I get it. Who wants an ugly data center right outside, you know, some Monmouth data center right outside your house. Right.

You know that you don't want that as your view. My fear is, though, that under the current administration, you know, no one cares about the spotted owls and the little darter fish or whatever they're called. They're just gonna drill baby Jill.

And, and it may very well be the same thing here. Mm-hmm. Chris.

Yep. I, i, there may be some of that too, but, you know, the, you know, look, I've been playing with critical infrastructure for 35 or so years, right? And this is a whole world of told you so, coming to roost, you know, 'cause mostly what I I see is, you know, the, the, the legitimate pushback, you know, and yeah, I agree.

Environmentally, we need to account for the cost. And in critical infrastructure of what everybody thinks about is the grid and the power grid. The North American power grid is an amazing beast.

It is a single creature of, you know, titanic proportions. It's been engineered over the last a hundred years into what it is and what it isn't is capable of, of supporting the kind of systems, the power usage profiles that are being proposed for a lot of these data centers. We talked about this last week with, uh, Dan O'Brien, I think.

But, uh, you know, they're using evaporative cooling, which is wonderful and cheap, as long as water is cheap and you can throw it away and turn it into mist, you know, to cool down your data center, which historically it's been for decades and decades, where we've seen these things. You know, they're universities and, you know, office parks. Um, not this time.

You actually have to account for all the costs. You know? And this is where, you know, ideologically all of the parties that I belong to, come to home to roos, because this is about writing it down, owning it.

And if I'm on a data center and I do, then I gotta figure out what the actual cost is, you know, not what the actual dollar is given. Whatever the current, you know, economic climate is, the whole thing's, the supply chain soup to nuts. And these power sy you know, you can't just pop up power grids.

These take decades and you can't on, let me give you a bit of hope at the end, that with the kind dollars we're talking about, yes, you can, you know, we all agree, you know, that, uh, well, I agree. And I know on this, on this show, we tend to agree that small nuke, you know, kind of things we're talking about, that's lovely. Uh, battery is lovely.

You know, there's lots of ways to do things. But at the heart of this story, I think is evaporative cooling unrealistic, you know, uh, applications of what's supposed to be cheap technology that just happens to cost the water we all drink, right? That's not good math.

That's not good capitalism. Well, I, you know, let me just say this. Is, these data centers are, are on a scale unbe before, you know, night, nothing we've seen before.

And, and frankly, all the water in the world isn't gonna cool. 'em, we need, we need next gen cooling solutions, right? When we talk about liquid, cool.

They're not talking about water. They're, you know, they're sort of more like antifreeze kind of things, but Well, it that needs more power. And I'm sorry, I didn't really make my point.

'cause when we agreed last week, if I, I, I'm pretty sure it was that. Anyways, we, we all need, we need more power, right? You need green.

You want, We Need all, we need lots of power. We Yes. Which needs power, which is more power than just using water.

Well, that's the, So it needs Even more Power. The, that's the position I'm gonna take on this, which is, this is the thing that will get the power grid upgraded, right? We all talk about our aging infrastructure.

You can't, you can't live on the infrastructure that we have now. It will force it to be upgraded. And so in that way, I'm all for it.

I, I totally get not in my backyard and get off my lawn kinda thing. Um, you know, I don't wanna be living next to a data center. Uh, but it's, it's a fact of life that's gonna happen.

And we're, we're driving down that road, so let's do it. Right? Let's really build out the infrastructure to support this, not just for today, but for tomorrow.

And it's not the worst infrastructure that, uh, this is really a story. I, I think, uh, you, you, you started down that path and one, which is, uh, we've seen this before. I mean, there's no context here.

This is, so the, the, the publisher of this research is a firm called Market Data, uh, sorry, data Center Watch, which is owned by a, um, an AI consulting company and, and a legit one. And I'm not saying they're bending numbers or anything like that at all, but where's the context for this? Uh, I think that there's an agenda here to, which is pretty explicit when it comes to data center Watch to expose nimbyism in data center built outs Yeah.

And activism. Yeah. You know, God, now that you mention it, I don't know if just Mike Ard didn't pull this up strictly to have us discussing it on the gang today.

See, As opposed, Well, he assigned the story. So Mike was the first thing, the other day story, I, I just, it's why is it not, why is this not a story? This is far older than Nimbyism.

This is like the oldest technological story ever. And it's certainly, uh, the oldest and original one in it, which is, you wanna do all this fun software stuff, you have to do hardware build outs, including resources and all that stuff. And sometimes we're gonna get to an example later where the hardware is way ahead.

In this case, the hardware is way behind. And, uh, uh, you know, there's no supply. There's not, there's insufficient supply for all the demand to build data centers, insufficient supply for appropriate real estate in, partly in part because a lot of these, they, they want to put data centers where there's already, you know, uh, a good network connection.

So they can't just put 'em up in the Arctic like they, they w would do otherwise. There's also power issues. There's all this stuff, right?

So, like Mitch said, it's gonna get built out. I don't think we have, we're missing two contexts here. One is what are we capable of building in terms of power, uh, uh, delivery, uh, globally?

We're just sitting here going, wow, we just don't have enough. It's a disaster. Ah, this guy is falling, or we're saying, shut up and get outta the way of innovation.

And, and, you know, let's steamroll over all the problems because of all the benefits that are gonna come. Um, so that, that's, you know, one side of how it's not a story. This is just supply and demand in, in action.

I would disagree on the sense that the level of scale here is phenomenal, and more communities are seeing this and experience in this, in the form of water issues. And they're starting to see electricity costs go up. And as Tip O'Neill once said, all politics is local.

And when you start messing around with the local stuff, you'd be surprised how much it will come around and kick you in the head. And, and frankly, yeah, Just compare it to, sorry Kate, I'll let you That's okay. Definitely let you go.

Just compare it to building roads takes freaking forever. Nobody wants to bulldoze neighborhoods. They don't want a big fat road right next to them, but they have a congestion problem.

So where's the difference? I just don't see it. God, you, Your Bronx upbringing is shining through.

To your point, though, in here in New York, we were talking about rethinking about how the Cross Bronx Expressway works because of Robert Moses bulldozed entire neighborhoods in the fifties and sixties. Exactly. They determined that the entire destroy the Bronx, right?

So that, that whole thing might become a tunnel. Who knows? And, and the only thing that, you know, talking about it not being a story for this non-story, it's actually appearing in a lot of different, um, you know, podcasts and, and other, uh, frankly, other, other news shows where you have, you know, citizens, you know, gathering together and just saying, you know, no, to these data centers.

I continue to believe that we have not seen, you know, I, I can't help but remember over and over again, walking through these huge, you know, main, where all the mainframes were, and then all of a sudden there was like one mainframe, and, and you saw the, the, you know, the, the footprint of where all these other, you know, uh, mainframes were, I worry that we're rushing to build really, really large data centers without yet fully understanding this capability of where we're going. So that's one major concern. I think looking at the cost more than just the, the financial cost, is it really important, especially with resources being where they are today.

We don't have unlimited resources. We all know that. So we do have to rethink how we do things.

And, and I don't think that that's a, a bad thing. And people getting upset about, you know, not in my backyard is legitimate. And we have to think about, you know, or how do we make something more aesthetically suitable?

You know, do we put like a nice park around it or through it or, you know, sounds, Incorporate sounds like something outta the New York City zoning commission. You could build the biggest building in the world, just put a couple of benches outside in some trees. com, right?

And I remember on using that, people complaining that dot coms are gonna destroy everything. And I think I sort of said what guy said, and it's, it turned out to be true, right? 001% is more than we're we're gonna get with academic budgets and so forth.

Right? You know, and, and Mitch, what you said about the, about the, uh, uh, as an old school power grid person, if you want to get tens of billions of dollars into your grid, suddenly this is the way all those projects you thought were a good idea now is to get approval and lay the, you know, copper Everywhere. But let, let's, let's just put this into the, there's a couple of different buckets y'all are hitting here.

You like, when I say y'all, Y'all, I do very much so say Appropriate in this context, Southern Long Island Accent. There's a couple of buckets here. Number one is the issue of the power, regardless of where we build the data center, we gotta bring the power to it, or we build the power there in the case of, let's say a, you know, container sized nuclear facility or something.

So you got power, you got the cooling. Kate, you bring up a point, and I'm not sure it got through to the audience, and, and the rest of us, which is we're building tomorrow's data centers for today's and yesterday's technology. Who knows what tomorrow's technology requirements are going to be?

Are we going to need a data center the size of Manhattan to do the kind of workload that we're anticipating gets done? It may be not the case, but anyway, so there's that aspect, there's the power aspect, and then there's the NIMBY aspect, right? I remember Why don't forget the connectivity too.

Well, I, I, yes. And, and connectivity fiber or however we're connecting, that's, that Is probably the most limiting factor because that's what's causing the 600 data centers in Virginia or whatever, Right? Be because it's the latency.

And I think it's also why we're seeing places that we wouldn't think of as data centers, data center central vie for this business, right? And it's gonna take all those things coming together for it. And if those people are willing to put up you, you know, I, I live in an area where if you development is near big transmission lines that your, your property prices go down.

Could you imagine? You know, who's gonna wanna live near these things? Anyway, we're about outta time for this one.

I, I would, I would just point out one last thing. There is a, you know, a a, an election cycle coming up in November. It'll be interesting to see how many local officials get turned out over data center issues.

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Something happened on the way to AI Nirvana, and it may be just the simple fact that a lot of this stuff is harder than anybody imagined. Uh, there's a new report out from OpenText talking about how 57% of folks now think that AI is a crucial project for them. That's down from other studies that had it up and around the 90% and more interesting to me is at least 57% also said that it's just hard and they're not ready.

And they got a lot of work to do, and there's a lot of data management issues. But Chris, did we kinda underestimate what's required here to succeed? Of course we did.

And I, you know, for those of you who are watching this in Clipse, you know, Alan set this up perfectly at the end of, uh, the, the last segment, because we're building the data centers, you know, on the whole data center issue. We're building the data centers today for today and yesterday, but the infrastructure is tomorrow. And this is the same issue, right?

You know, it's, it's, it's, you know, I'm, I'm talking to a monolithic ai, I don't know what I'm doing with it. You know, it's, it's using 8 million joules of power, you know, to, you know, organize my calendar. And yes, it's, but, but the marks of a successful, uh, wave, you know, it's not just a blip is no matter how bad the, the downsides are, you know, we end up, you know, doing the kinds of things we were talking about in the last segment.

We worked it out. So it's a mess. And I like this show.

'cause it gives me a sort of weekly reminder of, of where I've been on this path. And as we all know, like, you know, as of June this year, you know, the world has changed. You know, it's changing month to month, the data center plans, I think they're intrinsically wrong.

I said that on this show. You know, I think we're building the wrong sort of hardware because we're thinking about it wrong. You know?

And, and we're not even looking in the future a couple of years, you can run an AI on your laptop at home, right? On, on a small, not, not at, you know, it's not, but it's a remarkable what it'll do 3, 4, 5, 10 years from now. This isn't all about data centers and big ai the way we're thinking about it.

So there's so many ands to this, you know, answer to yes to your question that it's kind of hard to find a single place to stop. Mm-hmm. You know, Mitch, I seem to, you know, I think it was, it's been a couple years now that we had some sort of, uh, hackathon that we were all running and you were at.

And I remember looking at that and how amazed I was at how all these developers and people that I knew were really smart were like wrestling with the fundamentals of data management. And I was like, it seems like, um, maybe we'll have a renaissance in data management discipline and practices. But it feels like, you know, a lot of people just don't have the grasp of data engineering and what's required.

Well, the, uh, you know, data is the new oil, right? But oil is sticky and messy. It's not always fun to work with.

And, and that's the challenge is, is we don't really, uh, curate our data, you know, uh, comprehensively. So we can just dump it into a rag or into some kind of a database that, that, uh, AI can use. I think the other part of it, Mike, is, and then how are we gonna use it?

How are we gonna protect it? How is AI going to process it? Uh, what kind of applications are we using?

You know, I, I go back to, uh, you know, the, the idea that that AI is and will change everything. Well, that's a formula for, well, it's gonna be hard. It's not gonna be easy.

'cause when things change, you change how you work, you change how you think, how you solve problems. And while it may automate things and let you do more things, do, do, uh, more work for the output for the same amount of work that doesn't come for free. So we're in this, we're in this learning period, and it isn't the first time we've rushed headlong into the cloud or into blockchain, or into fill in your favorite technology that, that maybe worked out in the end, but wasn't, didn't, didn't deliver on every promise in the first six months of our, uh, our wild appetites to adopt it.

So data is something that I think is a, a discipline. Now I see job postings for more data scientists, more data curators, data analysts, people need that skill. So maybe we're doing a little less code gener writing, so we need more data people.

And in the article it says, um, Barry says that, um, con context is king. Mm-hmm. And it just makes me think with, with data as something I've always spoken about, like, we have data swamps, and just like those swamps in Florida and the south man, if you get in them, they'll kill you, right?

And so the, the data is, is one big data swamp that's, you know, gonna kill us if we don't, you know, really start to, to address, you know, context and, and you know, I'm, I'm all for, you know, this whole deep bloating, right? I, I, how, how many times do I have to say we, I think we just have too much data, and in so doing, we, we are not able to manage it. And I don't care how much, you know, we put towards it.

I, I think we have to, to look at data differently instead of doing the same old thing that's just not working. You know, there's, there's so much of this, you know, in the last segment, I, I got to pull out the, uh, you know, old OT person saying, you know, told you so, right? You don't put the money into the grid, you know, you're gonna get an ants someday.

Now we have ants, it'll cost you more. Katie, exactly what you just said. You know, this is data science and information technology people for the last 50, 70 years saying, you know, what we should be doing.

And, and the practicalities of business are, we don't. So we get swamps of data. And I, as I look, as we look at this, you know, given the tools, we have given AI as it is, and these semantic systems that, you know, give us, you know, a lot of speed and like, Mitch, to your point, they speed up a lot.

You know, you might have more work to do, but you're doing more work. Um, and, and what you, what we have to do is the same sort of thing that we do internally in our heads or as cultures, is not save everything. Lemme give you an example.

You know, we right now could wire up and we are wire UPIs with perfect recall of everything. And it turns out that doesn't work. Just like the same reason.

It doesn't work in your head. It's not because we're not good enough. It's because infinite information is nothing.

It has no value at all. So curating our data sets and putting them in context where you can realistically actually use them as opposed to just storing them is, you know, a told you so from every data scientist, I suppose the last 50 years, saying, that's what you get for filing things this way. Yeah.

Boy, Chris, I So go ahead, Kai. Sorry. There, there's, There's always a lot of talk in with new technologies, especially disruptive ones that clearly this is like the most disruptive technology we've ever experienced.

There's talk about complexity. Um, and I, I'm hearing a lot that, no, no one's used the word, so I don't wanna put the word in anyone's mouth, but that, um, taking advantage of AI in all its forms, whether development or a or just incorporation into services or apps or whatever is, is, you know, there's a lot to think about. But I think actually there's a difference between complexity and difficulty.

Um, something can be complex, uh, hard to imagine complex and easy, but something that's really simple to imagine can be really hard. Like, for example, you know, lifting a car with your own hands, that's really hard, but a simple action. So what I'm getting at is, um, and I keep coming back to the way AI is a simulation and not actual intelligence.

And because it's a simulation and it's designed point is to simulate thinking, it's really hard to understand how to use it and develop it. We keep coming up against this with, I think the actual experience of developing and using it, having a disconnect from what the expectations were going in. I kind of felt like being all kind, uh, like you be, you know, a slammer and go and say, oh, through, through it.

So I'm really surprised everybody sucks at developing ai. Um, but I think that, that it's following the same kind of learning curve as everything else does. It's just because it looks like it's so promising.

Um, there's more frustration there would be, I mean, what is it, three years now? And we have this, this huge proportion of enterprises trying to, trying to adopt and develop it. Maybe we just need a little more patience.

I, I think the point about expectations is exactly right. Right? Because our expectation is the sci-fi, you know, you know, thing where you walk up, you know, they find the ancient alien computer that knows everything, and it says, I have considered every thought in the universe, and we expect that.

But that's not, even when you think about how thought works, that's not logical. It's not possible for anyone, any, like, not, I mean any, I mean, billions of years from now, no one's ever gonna be able to do. That's not how thinking works.

And we expect these ais to do things that would do the super human model that we have in our head, which doesn't really exist. So it comes down to compression of information, not expansion, you know, getting it down to small enough bits so you can reference it like we do in our heads. And, you know, we've been modeling, you know, a a i, I won't repeat it.

You, you phrased it really well at the beginning of this. You know, these are not intelligence, but they're, they're a simulation up. So we're literally putting together code that simulates the parts of our brains and thinking about how that works.

You know, the compression that, that we use so we can access the infinite information of the, of the universe and walk around, you know, as agent creatures has a lot to say about what any AI we're gonna create is even capable of. They can't do more than we can. We, that's not the way thinking works.

I think Kate's spot on though. It is about the context. And I'll give you an example of something I was playing with, um, last few days.

I took the earnings call from a vendor, and I got the transcript and I shoved it in the chat, GPT, and I had it quote, craft a story. Then I listened to the actual earnings call myself, and I was, you know, the facts were in the story, but the context was gone. There was no, um, sense of, uh, where this all kind of fit or where it was in the larger Mike and then Mike.

And then wait, wait. And then I kept playing with the prompts to kind of tailor it to what I needed it to be and adding more data and all this other stuff. And after like an hour, I was like, I'm spending an hour crafting prompts for something I could have done in like 15 minutes.

And that's kind of where I'm like, eh, this isn't quite where it needs to be. But is that the AI's fault or is it the transcript of the call's fault? The transcript of the call was highly accurate.

It was com all the, every word that was said in the actual meeting was in the transcript. It's just the trans, but the transcript doesn't capture tone. It doesn't capture, um, any of The other.

And so are you blaming AI for that? Well, I'm not blaming it though. I'm just saying as a practical use case, though, it was gonna still gonna be faster for me to just look at the transcript or listen to the call and then craft something out of it than it would for me to sit around and tweak all these prompts to get to something that still wouldn't be a hundred percent.

I think what you're Bringing up, that's what I mean. Go ahead. What you're bringing up is, is pro, you know, call it prompting or using AI in that way, just as it is with, for developers.

It's a skill you've gotta develop. I mean, we've all been prompting with Google, right? We figured out our little human algorithms of how to get, you know, how do I get the LinkedIn page the easiest for somebody's name and company?

Why have a way of pasting that in really easy? And I, it will come up usually first. It's on a much greater scale.

There are different ways of prompting. There's like structuring things highly structured of JSON prompting. There's contract prompting and people are discovering different models, like different types of approaches.

But it's, it's around that context point that we brought up earlier is there's instructions and then there's what we know about solving the problem or, or what we need to solve the problem. And how do we encapsulate that in some way to give the AI better direction as well Taking, I think this is a prompt engineering problem. I think, Mike, what you are really saying is AI doesn't pick up the inferences, the nuances, the humanity.

That's, that's part of it. It's also how things are said. It's a combination of all those things, right?

It's partially what Mitch is saying, the prompts and the skills. And, and, and everybody on this call is pretty proficient with logic and how to put together prompts. It's, you know, but do that in the average employee workplace.

And those people are not gonna be sitting around going, let me become a prompt expert. That's just not gonna happen. And they're gonna look at this stuff and go, this is kind of broken, and they're not gonna drive it as hard as you think that they would.

That that was my, that was, that was a, a much nicer way of, of my reaction to what Mitch said. Mitch is taking a, a developer's and engineer's approach to it. And that's 1% at best of, of the user population.

Uh, it's just not a case of, um, it just in general of, uh, using something that, uh, does work for you. I don't know. Like it does do work for you, but it doesn't do good work for you.

It, it just can't. It just can't. It's, and if here's the, here's the other, here's what I'm getting at.

Mike. You have been doing this for years. You're a journalist and editor and so forth.

1% of the population and other people who sit there and go, oh, well, I can produce a report. I'm gonna start my YouTube, I'm gonna start my own substack, and I'm just gonna AI the crap out of this stuff. 'cause I'm really interested in it.

And that could produce volume. And a lot of the readership is not even gonna know that. It's not, I think we call that AI slop these days, right?

But this is an example of the layers of the problems because just that one thing and, and Mike we're building, you know, a, a, a AI that can hear the tone right now, but, uh, imagine giving just a transcript of a meeting to an intern as opposed to actually have them listen. And when you take context and tone out, this is what you get. So it's not that LLM models or the use case.

It is just that we have such a layer of inefficiencies and learning curves and, and dumb ways to do things. So I, I just, yes, we have a layer of inefficiency. You know what it's called humanity.

When you look at, and I, I, I love reading about Neanderthals and Deno de Deso and you know, how, how language and communication developed in humanity. It's not all from written languages. There were, there were, there were sounds, certain sounds not words conveyed, certain things, if you said a certain word by, but also making a hand gesture, it changed the meaning of the word, right?

This is how humans communicate. It's not all words. It's not just words.

And unless we're gonna make our AI savvy to that, you can't blame it for not being able to, to translate it, right? Until the point where we can have an ai, watch the video, listen to the words, and see the, and be able to make guesses at the inferences and the subtleties and the nuances. That's, that's the difference between communication and words.

Mm-hmm. I don't think anybody's blaming AI here. I think we're just coming to a conclusion that, you know, it, it has fundamental limitations, and I don't think we're abandoning it, but I think you really do need to think long and hard about how much time are you gonna spend trying to prompt something out of it versus just doing it yourself.

This is, that's the problem. It has fundamental limitations that are really hard for almost anybody to see. All right, we're gonna end it on that one.

We'll come back. We've got more to talk about, guys. Let's talk about a IPCs garbage.

I don't know. You're watching. Textron inc.

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Hey folks, we're back. And yes, we're talking about a IPCs. The Futurum Group has a new report out talking about what the latest rate of adoption is, and this is their second report on this topic.

And, well, not surprisingly, it may not be going as quickly as we thought, but there are people buying at least the lower end models of these things. Guy, tell us what's going on here with these things and where are we on this curve? Well, um, uh, terrific analysis by my colleague Olivier Blanchard, um, who's been covering, uh, AI and devices for, you know, since the beginning of this craze three years ago.

Um, one, one key thing to look at is, uh, that according to the research, uh, something like 90% of it, uh, uh, leaders, um, or at least you know, PC decision makers, um, I think that IPCs are, are worthwhile and, uh, the, the basic knowledge workers platform in the future, if not anybody using a pc. Um, why is that? Well, there's a craze going on.

People are using ai. Uh, just to be clear, like, I'm not gonna define the AI PC exactly here, but it's something that can do inference on the PC and on the device. So that also points to the problem and the problem.

I wanna pose the problem as a question to, um, well, to you, Mike, actually, it's equally for Mitch, uh, or anybody, which is, um, uh, how come developers can't gr client server 40 years in? Why is it that when I'm using a mobile app, which I, this is, uh, what I've been complaining about for 20 years at least, and, uh, uh, you know, I go into a tunnel or out of a service area, suddenly the app stops working even though I'm just doing something that, like maybe just writing an email or who knows what, um, they're better than they used to be. But somehow or other, um, we still wind up thinking that there's just one place like the mainframe computer on this USS enterprise where everything happens.

And in our case now it's the cloud. So what Blanchard points out is that, um, most AI is still developed and hosted and delivered from the cloud. So since a I PCs can't carry fiber with them wherever you go, there's a connectivity question and issue because that fun, uh, neural net, uh, inference chip on the PC with its associated memory and hardware is just not being utilized.

There's maybe a few dozen commercial off the shelf applications right now. I mean, some are really important and big, um, like Microsoft applications, Adobe applications, but there's, there's really not very many that utilize it and forget about, um, you know, the, where a lot of people and how a lot of people use apps right now, which is in SaaS, all of this processing is happening in big cloud data centers. So that's where the problem is.

They're being adopted at, at a lightning pace. I think 90% is pretty high, um, but there isn't the software there for it. And frankly, I despair, um, to a great degree of that happening anytime soon because, you know, after Client server, which was basically a way to overcome the limitations of the PC-based server at the time, ever since then, we've had the internet and we've had cloud and we've had all this stuff.

And it just seems to be yet another way for developers to do the easy rather than the good, which is to have their cute little development environments and not worry about distributed systems. Go. So, you know, guy, first of all, I want you reference these Star Trek Enterprise Mainframe.

I don't know if it was Julia Mainframe, but it was Quantum. But the Day Stream, if you remember the episode, the day stream computer, the, the day stream institute, remember they installed a, uh, Mitch, remember the, the PhD fella installed a, a, a beta of a new operating system and it kind of mm-hmm went outta control. It reminds me of these ai, I I, I forget what they, they, the, the computer had a name and uh, but it was kind of like an AI kind of thing.

But in any of it, I'll look it up and we'll put it in there. But here's, look, let's cut to the chase. The A i PCs won't be valuable till there's a killer app for it.

And right now there's not period. The end, there's no killer app. What, what, what am I getting on my A IPC that I'm not getting on my other PCs Max or Phones?

Yes. Thank you for saying that. That's a much better way to put it, which is the user doesn't know where the inference is happening.

They just know that it doesn't work and they're gonna bang away at the thing. 'cause it's not working. There's no, there's Nothing to it.

So, Mitch, wait a minute. Soft, Mitch, let's get Mitch in here for a minute. 'cause we know what we're kind of saying is, you know, software developers, you know, Mitch's people are holding the whole show up.

So what's up with that, my friend Here, we, here we go again. The famous word of Donald, Ronald Reagan and William Software developers for the issue. I thought you Were gonna Donald Trump there for a minute.

Well, you know, hey, that, that too, um, it is actually one of the big issues is, you know, you just don't put hardware in front of it and always get a great, you know, bump in, in performance or get a great new capability. You've gotta have the software take advantage of it. And developers are learning how to build applications with AI both in the cloud.

But now how, now that we've got this, uh, capability on, on the device, the, the best example is Apple. The one who's the farthest behind, at least publicly, right? They probably have the, have had the most inferencing power on the devices that we use, that who knows what it's used for, but it's certainly not an elevated or accelerated their AI strategy.

It's easy to dump on Apple right now. Maybe they'll have some meaningful announcements down the road around ai, but you know, software still is still eating the world. Folks, That, that's a perfect example of what I'm talking about.

I'm gonna start using that instead of the subway I used to use. I go into the subway and my game doesn't work. That's an example.

How many, you still have Siri even on, you know, the latest Apple hardware? Siri tells you I can't answer that right now. Why?

Because every freaking time you ask it anything, it's gotta go ping some server somewhere instead of just being able to, to do something locally. It just doesn't know how to do that. Why doesn't know how to do that with no is the wrong word.

It was not programmed to do that. Why not Mitch, tell me, tell me why are developers not doing this? Is it just too complicated to think about two environments running an application, it's just client server.

Where's the problem? Well, networks are like the future. Um, they're everywhere, but they're just uneven.

That's your problem in the subway. Uh, I'm gonna, I'm gonna take the opportunity, you know, for three, uh, separate segments to say this is another I told you so thing, right? You know, so we're gonna give people PCs, home PCs with 8 trillion lines of code and settings even at the, you know, the tab level where we actually have access to settings that none of us can decipher at all.

Um, and then that's how we get answered and that's how we get here. But I will say there was an open source project, Lumina os this is the last Windows device in my office here. That's still because I, I was tempted to, to switch over.

Uh, but yeah, you have to start off an entire different stack and it operates differently. You know, a a PC hardware is totally hobbyist right now. You gotta be totally into it.

We'll see. Things are moving fast maybe in a year. You know, people are running our, you know, open source take on it in a couple spots in the world and it seems to be going well, but it's, as we said in in every other, uh, segment today, there are a lot of layers to this.

It's not magic pixie dust. It will take a while to work through. But you know, as with power grids, as with, you know, how we've been storing data, um, you know, how we've been considering our personal device needs reconsidering.

If we really want it to be smart enough to do LLM tasks and things, we expect, you know, without some massive data center somewhere. But we can do it. One thing that the hype cycles and the AI adoption curves don't properly represent is in this adoption curve, there's a point beginning of that curve where we're using the technology, the advanced technology, whatever it is, cloud, IPCs, et cetera, AI to solve problems in the way we solved it before we had that capability, right?

We're using the kind of the same mental model of how we would apply it or the same methods, same software that we're building. And there's a point like in the cloud where we realize that, oh, I can do things a lot differently in terms of managing my infrastructure if I have, you know, three times the amount of servers available when I do my upgrades, I can do rolling upgrades, I can do AB testing and do lots of stuff that I weren't really possible before. Just as one simple example.

And you start to rethink and do things a a little bit sometimes greatly d differently. We're still in this solving the way we've been solving it with ai. We have a, that's why, you know, we struggle with prompting Mike.

That's why we struggle with why, why don't our apps have better AI in 'em yet? Why don't we have apps with AI or apps that are AI and why can't they connect? We're we're in the sub subway.

All of those, you know, we're in this early cycle. I'm not saying all those problems will be solved, you know, at once, but it's an evolution. So we're we wanting the benefit and we're still solving problems that we, that we, we got here.

So I have, I have question. So do you think I'm being too pessimistic? 'cause I just, I I just, this, I feel like this particular problem is different.

That there's some, there's some cultural or, or mental disconnect. Not mental, but there's some disconnect. I where like I'm saying like it requires development in two environments.

That just seems to be one too many. Yeah, I, I agree actually with Guy. I, I, I totally agree with you here, guy.

I mean, we, we need to, we need to, I don't know why we continue to even think about that whole two-way relationship. I think it's just, you know, let's start developing for that, for that specific chip for, for this ai chip on PCs and everything else. And forget about legacy.

Legacy is crushing us all the way around. I I I Know It's spoken like a real Mac person. I need To throw it out and rebuild from scratch fast among the things.

Yeah, let's get at AI and, and, and Mac and all that stuff. What about web assembly? Web Assembly is a way to do exactly this.

Go Waso To put a package on a Yeah. To put a PA and, and, and I'm like huge web assembly enthusiasm. Probably because I like tilting at windmills.

I mean, maybe, I'm not sure which is harder writing in Wasm or, and building an AI software. 'cause they're both kind of difficult, you know. Well, I think we're gonna have to continue, but I do have touche.

Okay, Fine. I I do have a question though. I have a question for the group.

So who here thinks that they want to have it's September, you know, and believe it or not, the holidays are two months away. So who wants an A IPC for the holidays? Who's got that on their list?

Anybody? Or are you gonna wait till next year? I Already have one.

No, I, I want it. Ah, I see. There you go.

There's all Kate. Kate just summarized the problem. We're all buying them because we're FOMO or something.

I already have three of them not counting my phone. Yeah, I ju I'm just a big talker. Yeah, More Hardware is always good.

Now, now you guys can start to, you know, now you know what to get me for. All right. Put it that on Kate's list guys.

We gotta, we gotta wrap up here. We're in 45 minutes and we've got a lot of text drunk TV to come today. Kate, guy, Chris, Mitch, Mike, thanks for joining.

Thank you for watching. Stay tuned for Techstrong tv. Um, if you're not watching this in our stream, uh, feel free to go wherever you want after this.

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I'm Alan Shimel. We're out.