The Global Economy and the Hype on Artificial Intelligence

around the world. So, according to the New York Times quote, Japan's benchmark index logged its worst single day point decline end quote. That index fell by more than twelve percent. Europe's markets saw investors freaking out and selling off assets, causing prices to tumble further. And while I'm writing this episode before the market's opened in the US, in fact, I think they just opened as I started recording this. The assumption is that our markets are going to follow

suit after stock futures took a tumble here in America. Now, tech companies were hit harder than other sectors were. Everyone was hit, but tech companies were hit particularly hard. But then tech has also been driving some pretty crazy growth in the recent past. They were kind of surging past everybody else, so they had further to fall as well. The semiconductor fabrication company TSMC, which is responsible for more than half the global market share in the semiconductor foundry industry,

saw it stock price fall by ten percent. Samsung Electronics same story ten percent decline. Bitcoin drop by more than ten percent as well. So what caused this Well, the instigating factor appears to be a US jobs report that showed unemployment rows to four point three percent, which is the highest it's been since twenty twenty one. There are fear of a recession in the United States. Seems like there have been fears ever since the pandemic. This would

continue to affect world markets. Obviously, and one other culprit was mentioned, at least in a CNN article on this topic. I do not know how realistic it is or how much weight we should give it, but that is our good old friend artificial intelligence. Now, to be clear, it's not really AI's fault. This is not a case where some AI process has cast the world economy into chaos, right, like some artificial intelligence algorithm played hanky panky with the

stock market and now everything's crashing. That's not what happened. This is not some science fiction black mirror episode. Rather, it's the perception of AI, the marketing around AI, the swell of greed around artificial intelligence, and the inevitable reaction when investors discover that perhaps the goose ain't so golden,

or at least it ain't golden yet. By that, I mean we're gonna talk about the good old hype cycle again, and I thought it would be a good idea to revisit the topic as we are seeing now how it can make a bad situation worse when hype is allowed to prolificate. So, first, to be clear, I don't think AI hype is the main reason for the economic crisis right I'm not saying that the excitement around AI goddess to where we are right now. I think at best,

it's just a contributing factor. It's exacerbating something that was already going to happen. But it is true that investors have started to back off of AI assets after seeing that there isn't a fast track toward profit, as investors have had to grapple with the fact that yes, AI is incredibly exciting and it has an insane potential. It is not, however, ready to be a massive revenue generator, and so it's like it's not fully cooked yet, and investors were like trying to rush it out to the

store and it's not even cooked. Well. There are tons of headlines out there about how folks have become a bit disillusioned when they realize that while AI could be boys to change everything, it's not currently doing that. Throwing AI into your business plan doesn't immediately met you insane returns. AI has limitations, including a very high cost of operation, and it's too unrealistic to say it's somehow just magically

going to cause revenue and profit to search. So investors could be cooling down from their initial excitement around AI, which means that businesses will have less incentive to just shove AI anywhere they can because it's not going to get their investors excited. But in turn, this could just be one contribution to this market instability, and ultimately it

may be a relatively small contribution. But it is certain that tech companies are seeing some of the biggest losses right now, and it's also true that a lot of tech companies jumped right on that hype train for AI. Now a cohesive look at all the factors contributing to the global economic situation is beyond the scope of the show. It is way beyond my ability to talk about it. I am by no means an economic expert, but we can definitely examine this smaller piece of that bigger's puzzle.

So let's do a quick overview of what the hype cycle is. And it's really the process that new ideas and often specifically technology typically follows, and it seems ridiculous that no one ever seems to learn the lessons. Though more on that in the second So first up, what we're specifically talking about has the formal name of the Gartner hype cycle. Gardner is a US based technology consulting firm that got into business back in nineteen seventy nine.

It was founded by Gideon Gartner, who was a business analyst, and the Gardner hype cycle, which I believe he was first proposed like in the mid nineties. It's kind of an observation. I think of it like Moore's law. We call it Moore's law, but Gordon Moore didn't call it that. The guy who actually came up with the idea. He was making an observation and then making predictions based off that observation. The Gardener HiPE cycle is kind of similar.

It's really more of a way of framing and contextualizing an observation about the path a new technology can take once it starts to reach a certain level of visibility, and the Gardner hype cycle describes five phases with regard to how the customer base perceives and uses this technology. Now, I should add a lot of folks have called out

the hype cycle for having some major flaws. One of the big flaws is that it's not actually a cycle, because you know, like a cycle's a circle, you end up back where you began eventually, because that's what circles do. The hype cycle is more like a wave, and the wave has a high peak of you know, your expectations you are are. In fact, it's called the peak of inflated expectations. We'll get to it, but that's where your perception of what this technology can do is far above

what the technology is actually capable of doing. There's a gap, and then you have a trough that's almost as low as the peak, typically the troth of disillusionment. That's where you come to grips with oh, this technology isn't as capable as I first imagined. And then after that you have a slow climb to a steady plateau, the plateau of you know, of productivity. But that's not a cycle,

that's just a path. However, beyond that criticism, other critiques include challenges to the observations validity in the first place, because there's a distinct lack of data supporting the hype cycle. It's one of those things that when you think about it, it kind of makes sense, like it feels like it it falls into the realm of common sense. But if you don't have any actual firm data to support these observations, just seeming like it's right isn't good enough. Not really.

I'm sure you've encountered situations where your own common sense told you one thing but it turned out that you were wrong. Well, that could also be the case with

the Gartner hype cycle. And also, as we go through this cycle, one really big flaw is that it doesn't give us much useful information on either how ideas move from one phase of the cycle into the next or what we can actually do with this information, Like it could just be hey, we keep doing this thing and I don't have any information on how this thing happens other than this has happened many, many times, but I

can't can't give you anything useful beyond that. That's one way to think about the limitations of the Gartner hype cycle. All right, so let's walk through it. The cycle identified five phases of a technology. So first up is the technology trigger. This is the initial event that introduces a technology ultimately to the general public. It doesn't have to

be a brand new technology. It could be a new way to apply an existing technology, or it could be some new aspect of an existing technology that gets added in, but generally we're talking about a pretty new idea here. Now. At first, not many people are going to know about

this idea, so visibility of the tech is low. Your group of initial folks who are talking about this technology start to get other people excited about it, and that's probably going to first include colleagues and peers, and then immediately after that investors, because you always want to try and get money for the cool idea you have, And then enthusiasm gradually starts to build. The text visibility increases as more people become aware of it and become excited

about it and talk it up even more. The people who invested in it, they have an insidi to talk it up. Right, if you invest in something, you want other people to invest in it too, so that your investment has a better chance of paying off. So you get your money in first, right, because you're smart. You want to get in when it's the bar is at it's lowest. You pour in as much money as you feel comfortable with. Then you talk it up trying to

get other people to get excited. And either the technology is going to ultimately deliver on what it promised and you're going to get a payout because of that, or you know you're going to fake it till you make it and you get a pay out down the line. Maybe someone bigger comes along and buys up the company that you invested in, and then you get paid out, Like you just want to get paid out, and there are a lot of opportunities to get paid out. So

that's why getting in early is a big deal. But we start to see a rapid ascension in visibility as media begins to report on it. Folks are talking about it, and ultimately the text hits kind of a saturation point for awareness, and the tech then is said to be at phase two, which is the peak of inflated expectations. It's where people are the most most excited for this new technology and speculation is running wild about how this

tech is going to change everything. And it's at this stage where people expect this new technology to do things it simply will not be able to do. Maybe one day it can do some version of that thing, but it certainly can't do it right away. So with AI, you could say the belief that AI is ready to transform business across every industry and produce instantaneous results, that would be the peak of inflated expectations. It's not that the tech may not ultimately get to that point, but

it can't do it now. Businesses are still grappling with how to integrate AI in ways that make sense to their workflow and operations. To use an analogy, if I were a hiring manager and if I hired on a brand new employee, a human being, and this human being is incredibly skilled, and they're knowledgeable, and they have an amazing work ethic, the hiring manager I would expect this employee to make significant contributions to corporate success. Down the line.

But I wouldn't think that they would just transform the organization overnight unless this was like some weird production of the musical How to Succeed in Business Without Really Trying or something. Yeah, if it's a fantasy film or something like that, sure, but in the real world, no, even the best higher in the world is it going to transform the business overnight. It's going to take time. Well, we should hold AI to the same set of standards.

You know, it's not something that's just magically going to transform everything. Okay, that's just the first two phases of the hype cycle. We've got three more to get through. But before we do that, let's take a quick break to thank our sponsors. Okay, we're back. So the world realizes that the hyped technology from the first two phases cannot meet the peak of inflated expectations, and folks start

to get disappointed and excitement and enthusiasm begins to drain away. So, according to the hype cycle, visibility now goes into decline. People move on to do other things. Some folks will stick with the technology typically, I mean it's rare that everyone abandoned ship. If they did, then that would just be the end of that story, right. The technology just wouldn't have any support. No one would be putting any money into R and D. It just it'd be a

dead end most of the time. Though there are those who stick with it, especially those who never really hyped up what it was capable of in the first place. They had more measured expectations. The initial ground swell of support, however, is gone. The tech then moves into the troth of disillusionment. So in this phase you see a lot of sad stuff happen because startups that launched during the earlier hype

phase face a really tough reality. Unless they can convince stakeholders to hang on, they may find themselves out of investment money and they just fade away. So a lot of startups fail during this phase if you subscribe to the Gartner hype cycle model. So this really did happen with a lot of VR companies back in the nineteen nineties. I'll talk more about VR a little bit later in this episode, but we actually saw this kind of play out.

In fact, you could argue VR was one of those technologies that inspired the creation of the hype cycle in the beginning anyway, but even larger companies that existed before the tech had hit the scene may find themselves in trouble if they invested too heavily in whatever the technology was. So with AI companies like Intel, Meta, Google, and Microsoft, they kind of fall into this category like they're huge,

and they're much bigger than just artific intelligence. That's not the only business they're in, right, It's not like they're a startup that's totally focused on AI, but they've also spent an enormous amount of money in AI research, and for interest and enthusiasm around AI to kind of fade is bad news for them because that's one less thing they can hype up to their investors when it comes to things like earnings, calls, and such to get them

excited and reinvest in the company. Gradually, according to the Gartner hype cycle, folks start to figure out the best uses for the technology in question, whatever it may be. These uses might not be as transformative and impactful as what people believed, especially at the height of the peak of inflated expectations, but assuming the technology has any utility and value to it, enough folks will stick with it. It'll find its place and gradually and with much less hoopla,

folks will adopt this technology. This Gartner calls the slope of enlightenment. People take a more grounded approach to implementing this tech. The slope of enlightenment then leads to the final phase in the Gartner hype cycle, called the plateau of productivity, where folks and organizations make regular use of this technology in ways that just makes sense. And this technology might not boost company profits into overdrive, but they would improve results in various ways. That's the hype cycle,

which again is not a cycle. Now, as I mentioned earlier, this observation is really just that it's not a law, and some argue it's not even an accurate observation that technologies don't necessarily follow this pattern that Gartner has laid out. Some may argue that it's more accurate to say it's the marketing around technologies that follow at least a variation of this story, but that the tech itself really should

be considered separately. Also, the terminology used in the Gartner hype cycle gets pretty wishy washy because it's not quantitative. You cannot measure it right. Inflated expectations, disillusionment, enlightenment, these words have no objective meaning or means of measurement when it comes to a technology's success or even acceptance. Also, it's pretty hard or even impossible, to tell where any technology might be along this cycle until it moves on

to the next phase. Right, how can you say, oh, we're at the peak if then next week it goes even higher like oh, I was wrong, Now we're at the peak, And you might be doing that over and over again. And as I mentioned before, another really big issue is that this doesn't identify anything that actually causes

the technology to transition from one phase to the next. So, at least according to some critics, not only can you not tell where along this supposed hype cycle technologies may fall, you don't know when they're going to make the turn to the next stage, and you don't know how dramatic that next stage is going to be. It's possible that the trough of disillusionment isn't as big a dip, or it could be a much worse depth like NFTs, I

would argue, went through a really big one. After they hit a really tall peak, they went to a really deep trough. And it's not like NFTs don't exist now but boy, they are not anywhere close to the level of popular that they were at the height of the NFT frenzy. You could also argue some technologies don't seem to follow this path at all. I think consumer smartphones fall into this camp, Like when Apple launched the iPhone.

Smartphones had existed before the iPhone. Apple is known as a company that launches refined products, as in products that have already been on the market in some other form, but Apple has put its own refinement on that technology, and that's where a ton of the value comes from. So when Apple launched the iPhone, even though the iPhone was not the first smartphone to ever hit the market, it was the first one that I think was really marketed to the average consumer as opposed to executives and

early adopters. Well, I think the hype just went up and up and up with the iPhone and with each subsequent iPhone release for quite some time, until we reached a point where the improvements from generation to generation were bound to be incremental rather than revolutionary, which was gonna happen. Right. You can't have every phone reinvent the phone every single time. It might work for the first few generations simply because

of the rapid development of technology. But you eventually start getting diminishing returns, and so then you get the incremental improvements. But I don't think we ever really entered a trough of disillusionment with consumer smartphones. I don't think that happened. I think people just sort of entered into like a

realm of managed expectations. For the most part, you still see people get excited every fall, hoping that the next Apple iPhone event is going to be one that blows us all out of the water by introducing some feature no one ever even imagined, which is an unrealistic expectation. It may still happen on occasion, but it's unrealistic. So I don't think consumer smartphones followed the hype cycle, so

not every technology goes along this path. Still, While I share skepticism that the hype cycle is the be all, end all description of the phases in technological acceptance and adoption, I do think it provides a useful starting point for discussions around technology that have experienced a skyrocketing early effect followed by a quick dip once folks realize that the tech perhaps is not fully baked. So I already mentioned

VR virtual reality as an example of that. In the late nineteen eighties, virtual reality was just starting to get attention. You know, Jaron Lanier, who is typically credited as the person who coined the term virtual reality, did that somewhere around nineteen eighty seven, so it's a pretty recent tech, and the concept from the get go was intriguing. Instead of staring at a computer screen and interacting through mouse and keyboard, the future of computing would put you inside

the programs. It was like Tron, but for real. Okay, for people who aren't old. The original Tron film came out way back in nineteen eighty two, and it featured a human character getting digitized and uploaded into a computer as a program, so he was inside the computer. That's kind of how people thought of virtual reality in the early days. So imaginations ran wild with the idea of VR. You'd be able to navigate operating systems and programs the same way as you would walk around like a building

or even a city landscape. Never mind that that isn't really efficient or practical. The concept of that really appealed to people, and you can see reflections of this to this day and other ideas like the metaverse. You still have people holding on to this idea that somehow navigating computer programs as if they were physical landmarks is appealing.

I'm no longer convinced that it is. But anyway, you'd be put into the middle of all this stuff, whether it was to blast polygonal aliens or perform surgery on a patient who's half a world away. The sky was the limit with virtual reality. There was a ton of money thrown at VR in those days. R and D departments were flush with cash, and a lot of folks started doing really cool research in VR. But all was not well. A few companies rushed to develop consumer VR experiences.

That equipment was far too large and expensive for anyone other than the very wealthy to own it for themselves, so the idea wasn't to create consumer products. Instead, the idea was to create VR arcade experiences. So you had companies like Virtuality building these enormous gaming rigs that included bulky head mounted displays and pedestals with a railing built in so that it would prevent players from stepping off and falling over. Arcade operators would charge players to play games.

Sometimes it was a flat fee for a specific game title. Sometimes you were paying for like five minutes of game time per go, which seemed expensive, but then if you spent five minutes in VR, you'd be like, no, I'm good. But players who took the plunge like yours truly. I was one of these people. There was a mall. Doesn't it no longer really exists. I mean the building does,

but there's hardly any businesses in it. But when that place mall was the big mall that was within an hour's drive of where I grew up, and we would go there on occasion, and they had a VR arcade in that mall, and people who tried it out, including myself, often we would be impressed with the user interface experience because being able to look around and change your point of view in game by physically moving your body, that

was a really big deal. You know, first person shooters weren't really a genre yet when these VR systems first

hit the market in like nineteen ninety one. Wolfenstein three D, which really set the stage for first person shooters, came out in nineteen ninety two, and you couldn't even really aim up or down in that just kind of left and right, So being able to move around a virtual environment and to control your perspective just by turning your head or squatting down or whatever, that was a big deal. But the graphics were really primitive, and that's putting it lightly.

They were blocky, and on screen characters typically had only a few points of articulation. This was necessary at the time because of the massive amount of processing power needed to make everything work. By massive, by the way, I mean relative to the capabilities of the time. Today, the requirements of a nineteen ninety one era VR game would be trivial. You could probably run it on your phone, but back in nineteen ninety one it required a lot.

As folks realized the limitations of VA are at this stage, excitement kind of puffed out of existence. So there had been all this media hype, especially like in movies, where the concept of VR became like an integral part of the plot of films, and then people realized, oh, that's not actually where VR is. It's nothing close to what

we've been thinking about. And with excitement and enthusiasm fading away, the money followed, so research labs that had been enjoying support suddenly were scrambling to stay afloat, and a lot of companies and labs would either go out of business

or they had to pivot to something else. Folks who were determined to use VR to tackle issues like treating mental issues like I know ones that were used for psychology purposes and used to treat things like phobias, You would use VR to do kind of an immersion therapy where the trigger the person was afraid of could be introduced virtually and the person would still have the reaction to whatever their fear was of, but they would know that they were in a safe space ultimately, and it

was a way to have immersion therapy without a real danger being present or a perceived danger actually being present. And it was an incredibly interesting area of research, but the money started to go away. Folks who were determined to keep using VR had to scrape for every penny and would often have to repurpose gear that was made for other purposes, primarily stuff like gaming systems, in order

to keep going. And it would stay like that until the two thousands, when VR would experience a more measured amount of support. Now, the VR story gets more complicated and it doesn't really fit the hype cycle format. Easily once we get past that initial part of the story of the peak of inflated expectations in the trough of disillusionment. I'll talk more about that in just a moment, but

first let's take another quick break. Okay, before the break, I said the VR story doesn't fully fit the hype cycle, at least not perfectly. The initial part of it seems

to quite well, right. VR as a concept starts to break into mainstream consciousness after being kind of the realm of engineers and computer scientists and various technical conferences, and then people get really excited, Hollywood gets really excited, and then folks get to experience it, and that excitement goes away and the investment goes away, and VR nearly died

as a result. But despite the fact that you had a lot of hype and you had this rug pole moment where support rapidly disappeared, the technology did build its way up again, kind of like that slope of enlightenment story with the hype cycle. But VR's relationship with other technologies like mixed reality, which brings augmented reality into it, that makes VR too complex for the cycle to accommodate, because it's not like it's a single story, it's part

of a multi branch story. It's most things in our lives fall into this, right I like to think I shouldn't say we. I like to think of history as a series of stories, But in reality things are so complicated you rarely ever have a true beginning, middle, and end, which is unfortunate for people like me who really like to have that kind of structure and closure. But that's not how reality works. Well that's not how VR works either.

VR is deeply integrated into other disciplines, and other disciplines are a big part of VR. So you can't really talk about VR as a specific technology, right It depends upon a lot of other technologies, many of which are are far more mature than VR is, and these technologies have proven themselves, so it is too complicated to just reduce down to VR is a technology. So critics could argue that the hype cycle is fine for contextualizing the initial era of VR, but that doesn't really hold up

once you get past that. And the same might be true for artificial intelligence. Certainly, AI is not a single technology, right like we often talk about it. Even I often talk about AI as if it were just a single tech like you could go into a big box store and buy a package of AI. That's not accurate, it's not realistic. Often it's because we oversimplify in an effort to try and tackle a really complicated and diverse discipline. That's what AI is. It's a discipline, and in fact,

many other disciplines feed into or overlap with AI. And there are a lot of different implementations for artificial intelligence. So generative AI gets a lot of the attention right now because it's flashy and it's easier to demonstrate than a lot of other AI applications. To the average person, your typical human being can easily get a grip on what generative AI is all about. They can play with

it online. You can have conversations with chadbots, you can have an AI artist generate an image based upon your prompts. You can even have AI generated video and music. And I'm sure you've all seen cases where someone seemed to equate generative AI with all artificial intelligence technologies, but that's extremely reductive. To say that generative AI is the whole

of artificial intelligence is just wrong. There are tons of different implementations and applications of artificial intelligence, so and many of them have nothing to do with generating any kind of content the way generative AI does. Now. I can't speak for all tech communicators, but personally I found the hype around AI this most recent round because again, AI also, it's a discipline that's been around for more than half a century at this point, so to call it like

new is crazy. It's been around for longer than I've been alive. But I guess if you're looking specifically at generative AI, even though that's been around for quite a long time too, the most recent focus is relatively recent. But I found it really frustrating because it's not that I think AI isn't useful or interesting. I do. It's just I'm so tired of seeing AI evangelists talk about artificial intelligence as if it's already a mature discipline capable

of instantly transforming the world. Parts of artificial intelligence, I would argue, you are very much a mature discipline, But they might have limited practical application for real world results. They might be more interesting in a computer science context than in a practical application context, which isn't to say that they won't be incredibly important, just that you're talking about foundational building blocks that are going to be used

to build the next really cool implementation. But I'm really tired of companies rushing into AI implementations without actually considering what value, if any, those implementations add, and how best to integrate them so that they actually enhance the company's business. In most cases, I think AI ends up being a distraction at best and harmful at worst. Now that's not to say there are no businesses out there doing it right. I think there are businesses that are doing this the

right way. But doing it the right way is hard. It takes a lot of planning, and I feel like a lot of companies are trying to take shortcuts out of fear of being left behind if they drag their feet on artificial intelligence, and that does not play out well very often. Honestly, it's really making me think of stuff like Web three and the metaverse and NFTs to go back to that, I think all three of those concepts have already gone through at least one round of

the trough of disillusionment. If we were to apply the Gartner model here NFTs, I would argue you had the most spectacular fall as unlike the metaverse or Web three NFTs were actually a thing and you could implement them. People are still arguing about what Web three or metaverse even means or what they will look like when fully realized.

But NFTs existed, they were inflated like crazy, you know, hype reached a frenzy, and then the bottom dropped out when people seemingly woke up and said, what the hell are we doing for the metaverse and Web three. The decline in the excitement has meant that companies that are determined to work on those projects are doing so an

increasingly skeptical and sometimes outright hostile environment. Meta has spent billions of dollars in the area of developing the metafor and investors have made it no secret that they are not convinced that the metaverse is going to be the next big thing. But since the digital ads business has recently been doing much better, I think investors are willing

to look the other way with Meta. I imagine a lot of them wish Meta would just stop with the whole metaverse thing, but as long as it's not actually harming the bottom line too much, then they're okay with it. But maybe Meta's long term plan will actually pay off, because goodness knows, I criticize companies for sacrificing the long

term in favor of short term results. So if Meta is able to create something that people actually want to use, then I think in retrospect people will say that Meta's investment was worthwhile, despite the fact that it made a lot of investors ant see. They'll say Meta was visionary and the investors were short sighted. And you can say that in retrospect. Right now, it's a lot harder to

say that for sure. We don't know if the metaverse gambit is going to ultimately pay off, or if it's just going to end up being something that ends up being a curiosity that spent costs billions of dollars. Well, I think the same thing could be true for AI.

I think that in the long run, AI is going to be incredibly helpful in many ways, but it may not be as transformative as folks were saying earlier this year and last year, and it may require a lot more customization to optimize AI applications for specific companies and functions. In other words, it might not be so easy as just throwing the AI switch and letting the money flood in. In the meantime, it looks like we'll be in for more rough waters. With the fears about the economy, I

personally worry about more industry layoffs across tech. We've seen so many already in the past couple of years. Intel recently announcing that it's going to let go of thousands of people, like fifteen to nineteen thousand employees over the course of layoffs in the very near future. We see it all over the place in the video game industry,

with some entire studios getting shut down. If the tech companies see their stock prices tumble, and that is what's happening, we could be in for more of the same across multiple companies. I would be shocked if we don't have a lot more stories in the back half of this year about layoffs and more cutbacks. And I think in large part it's because of this diminished enthusiasm and faith in the tech industry in general and the economy in

general as well. And that's terrible news. I really hope anyone who's affected by layoffs lands on their feet very quickly. It stinks. I've been there. It is a terrible feeling and it can really make you question everything, because especially for people who have really poured a lot of their time and effort into a job making it a career, for that to come to an end, it can be devastating. So my heart goes out to anyone affected by these kinds of things, and I'm hopeful that it won't be

as bad as I fear it will be. So I have two different I've got the Angel on one shoulder being hopeful and the devil on the other shoulder being fearful. And I really hope the angels win on this one. They're taking the outfield. From what I understand, I don't know. I never saw that movie, so I'm pretty sure that was a baseball film. That's it for this retrospect on the Gartner hype cycle, how it applies to artificial intelligence, or even if it applies, and I'll be interested to

see how things play out in the near future. I really do question whether AI is going to be cited as a massive factor for any kind of economic uncertainty in the tech sphere. I think it'll be a scapegoat for some of that, because I think these are issues that are bigger than just the disillusionment around AI. I do think that that's contributing. I personally don't think it's like a lynchpin feature of what we're seeing unfold now.

All right, y'all stay safe out there. I wish you all health and happiness, and I will talk to you again really soon. Tech Stuff is an iHeartRadio production. For more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts, or wherever you listen to your favorite shows