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Speaker 1: Welcome to tech Stuff, a production from iHeartRadio. Hey there,

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Speaker 1: and welcome to tech Stuff. I'm your host, Jonathan Strickland.

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Speaker 1: I'm an executive producer with iHeart Podcasts. And how the

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Speaker 1: tech are you. I read the news today, Oh boy,

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Speaker 1: I woke up to really glum global financial news as

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Speaker 1: various news outlets reported on how a fear that the

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Speaker 1: US market was slowing down is now having massive repercussions

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Speaker 1: around the world. So, according to the New York Times quote,

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Speaker 1: Japan's benchmark index logged its worst single day point decline

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Speaker 1: end quote. That index fell by more than twelve percent.

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Speaker 1: Europe's markets saw investors freaking out and selling off assets,

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Speaker 1: causing prices to tumble further. And while I'm writing this

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Speaker 1: episode before the market's opened in the US, in fact,

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Speaker 1: I think they just opened as I started recording this.

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Speaker 1: The assumption is that our markets are going to follow

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Speaker 1: suit after stock futures took a tumble here in America. Now,

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Speaker 1: tech companies were hit harder than other sectors were. Everyone

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Speaker 1: was hit, but tech companies were hit particularly hard. But

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Speaker 1: then tech has also been driving some pretty crazy growth

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Speaker 1: in the recent past. They were kind of surging past

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Speaker 1: everybody else, so they had further to fall as well.

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Speaker 1: The semiconductor fabrication company TSMC, which is responsible for more

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Speaker 1: than half the global market share in the semiconductor foundry industry,

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Speaker 1: saw it stock price fall by ten percent. Samsung Electronics

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Speaker 1: same story ten percent decline. Bitcoin drop by more than

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Speaker 1: ten percent as well. So what caused this Well, the

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Speaker 1: instigating factor appears to be a US jobs report that

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Speaker 1: showed unemployment rows to four point three percent, which is

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Speaker 1: the highest it's been since twenty twenty one. There are

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Speaker 1: fear of a recession in the United States. Seems like

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Speaker 1: there have been fears ever since the pandemic. This would

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Speaker 1: continue to affect world markets. Obviously, and one other culprit

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Speaker 1: was mentioned, at least in a CNN article on this topic.

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Speaker 1: I do not know how realistic it is or how

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Speaker 1: much weight we should give it, but that is our

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Speaker 1: good old friend artificial intelligence. Now, to be clear, it's

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Speaker 1: not really AI's fault. This is not a case where

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Speaker 1: some AI process has cast the world economy into chaos, right,

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Speaker 1: like some artificial intelligence algorithm played hanky panky with the

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Speaker 1: stock market and now everything's crashing. That's not what happened.

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Speaker 1: This is not some science fiction black mirror episode. Rather,

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Speaker 1: it's the perception of AI, the marketing around AI, the

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Speaker 1: swell of greed around artificial intelligence, and the inevitable reaction

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Speaker 1: when investors discover that perhaps the goose ain't so golden,

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Speaker 1: or at least it ain't golden yet. By that, I

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Speaker 1: mean we're gonna talk about the good old hype cycle again,

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Speaker 1: and I thought it would be a good idea to

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Speaker 1: revisit the topic as we are seeing now how it

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Speaker 1: can make a bad situation worse when hype is allowed

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Speaker 1: to prolificate. So, first, to be clear, I don't think

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Speaker 1: AI hype is the main reason for the economic crisis

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Speaker 1: right I'm not saying that the excitement around AI goddess

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Speaker 1: to where we are right now. I think at best,

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Speaker 1: it's just a contributing factor. It's exacerbating something that was

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Speaker 1: already going to happen. But it is true that investors

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Speaker 1: have started to back off of AI assets after seeing

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Speaker 1: that there isn't a fast track toward profit, as investors

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Speaker 1: have had to grapple with the fact that yes, AI

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Speaker 1: is incredibly exciting and it has an insane potential. It

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Speaker 1: is not, however, ready to be a massive revenue generator,

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Speaker 1: and so it's like it's not fully cooked yet, and

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Speaker 1: investors were like trying to rush it out to the

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Speaker 1: store and it's not even cooked. Well. There are tons

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Speaker 1: of headlines out there about how folks have become a

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Speaker 1: bit disillusioned when they realize that while AI could be

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Speaker 1: boys to change everything, it's not currently doing that. Throwing

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Speaker 1: AI into your business plan doesn't immediately met you insane returns.

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Speaker 1: AI has limitations, including a very high cost of operation,

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Speaker 1: and it's too unrealistic to say it's somehow just magically

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Speaker 1: going to cause revenue and profit to search. So investors

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Speaker 1: could be cooling down from their initial excitement around AI,

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Speaker 1: which means that businesses will have less incentive to just

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Speaker 1: shove AI anywhere they can because it's not going to

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Speaker 1: get their investors excited. But in turn, this could just

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Speaker 1: be one contribution to this market instability, and ultimately it

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Speaker 1: may be a relatively small contribution. But it is certain

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Speaker 1: that tech companies are seeing some of the biggest losses

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Speaker 1: right now, and it's also true that a lot of

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Speaker 1: tech companies jumped right on that hype train for AI.

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Speaker 1: Now a cohesive look at all the factors contributing to

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Speaker 1: the global economic situation is beyond the scope of the show.

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Speaker 1: It is way beyond my ability to talk about it.

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Speaker 1: I am by no means an economic expert, but we

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Speaker 1: can definitely examine this smaller piece of that bigger's puzzle.

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Speaker 1: So let's do a quick overview of what the hype

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Speaker 1: cycle is. And it's really the process that new ideas

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Speaker 1: and often specifically technology typically follows, and it seems ridiculous

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Speaker 1: that no one ever seems to learn the lessons. Though

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Speaker 1: more on that in the second So first up, what

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Speaker 1: we're specifically talking about has the formal name of the

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Speaker 1: Gartner hype cycle. Gardner is a US based technology consulting

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Speaker 1: firm that got into business back in nineteen seventy nine.

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Speaker 1: It was founded by Gideon Gartner, who was a business analyst,

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Speaker 1: and the Gardner hype cycle, which I believe he was

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Speaker 1: first proposed like in the mid nineties. It's kind of

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Speaker 1: an observation. I think of it like Moore's law. We

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Speaker 1: call it Moore's law, but Gordon Moore didn't call it that.

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Speaker 1: The guy who actually came up with the idea. He

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Speaker 1: was making an observation and then making predictions based off

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Speaker 1: that observation. The Gardener HiPE cycle is kind of similar.

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Speaker 1: It's really more of a way of framing and contextualizing

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Speaker 1: an observation about the path a new technology can take

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Speaker 1: once it starts to reach a certain level of visibility,

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Speaker 1: and the Gardner hype cycle describes five phases with regard

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Speaker 1: to how the customer base perceives and uses this technology. Now,

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Speaker 1: I should add a lot of folks have called out

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Speaker 1: the hype cycle for having some major flaws. One of

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Speaker 1: the big flaws is that it's not actually a cycle,

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Speaker 1: because you know, like a cycle's a circle, you end

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Speaker 1: up back where you began eventually, because that's what circles do.

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Speaker 1: The hype cycle is more like a wave, and the

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Speaker 1: wave has a high peak of you know, your expectations

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Speaker 1: you are are. In fact, it's called the peak of

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Speaker 1: inflated expectations. We'll get to it, but that's where your

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Speaker 1: perception of what this technology can do is far above

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Speaker 1: what the technology is actually capable of doing. There's a gap,

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Speaker 1: and then you have a trough that's almost as low

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Speaker 1: as the peak, typically the troth of disillusionment. That's where

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Speaker 1: you come to grips with oh, this technology isn't as

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Speaker 1: capable as I first imagined. And then after that you

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Speaker 1: have a slow climb to a steady plateau, the plateau

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Speaker 1: of you know, of productivity. But that's not a cycle,

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Speaker 1: that's just a path. However, beyond that criticism, other critiques

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Speaker 1: include challenges to the observations validity in the first place,

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Speaker 1: because there's a distinct lack of data supporting the hype cycle.

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Speaker 1: It's one of those things that when you think about it,

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Speaker 1: it kind of makes sense, like it feels like it

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Speaker 1: it falls into the realm of common sense. But if

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Speaker 1: you don't have any actual firm data to support these observations,

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Speaker 1: just seeming like it's right isn't good enough. Not really.

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Speaker 1: I'm sure you've encountered situations where your own common sense

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Speaker 1: told you one thing but it turned out that you

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Speaker 1: were wrong. Well, that could also be the case with

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Speaker 1: the Gartner hype cycle. And also, as we go through

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Speaker 1: this cycle, one really big flaw is that it doesn't

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Speaker 1: give us much useful information on either how ideas move

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Speaker 1: from one phase of the cycle into the next or

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Speaker 1: what we can actually do with this information, Like it

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Speaker 1: could just be hey, we keep doing this thing and

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Speaker 1: I don't have any information on how this thing happens

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Speaker 1: other than this has happened many, many times, but I

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Speaker 1: can't can't give you anything useful beyond that. That's one

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Speaker 1: way to think about the limitations of the Gartner hype cycle.

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Speaker 1: All right, so let's walk through it. The cycle identified

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Speaker 1: five phases of a technology. So first up is the

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Speaker 1: technology trigger. This is the initial event that introduces a

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Speaker 1: technology ultimately to the general public. It doesn't have to

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Speaker 1: be a brand new technology. It could be a new

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Speaker 1: way to apply an existing technology, or it could be

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Speaker 1: some new aspect of an existing technology that gets added in,

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Speaker 1: but generally we're talking about a pretty new idea here. Now.

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Speaker 1: At first, not many people are going to know about

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Speaker 1: this idea, so visibility of the tech is low. Your

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Speaker 1: group of initial folks who are talking about this technology

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Speaker 1: start to get other people excited about it, and that's

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Speaker 1: probably going to first include colleagues and peers, and then

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Speaker 1: immediately after that investors, because you always want to try

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Speaker 1: and get money for the cool idea you have, And

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Speaker 1: then enthusiasm gradually starts to build. The text visibility increases

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Speaker 1: as more people become aware of it and become excited

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Speaker 1: about it and talk it up even more. The people

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Speaker 1: who invested in it, they have an insidi to talk

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Speaker 1: it up. Right, if you invest in something, you want

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Speaker 1: other people to invest in it too, so that your

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Speaker 1: investment has a better chance of paying off. So you

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Speaker 1: get your money in first, right, because you're smart. You

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Speaker 1: want to get in when it's the bar is at

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Speaker 1: it's lowest. You pour in as much money as you

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Speaker 1: feel comfortable with. Then you talk it up trying to

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Speaker 1: get other people to get excited. And either the technology

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Speaker 1: is going to ultimately deliver on what it promised and

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Speaker 1: you're going to get a payout because of that, or

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Speaker 1: you know you're going to fake it till you make

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Speaker 1: it and you get a pay out down the line.

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Speaker 1: Maybe someone bigger comes along and buys up the company

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Speaker 1: that you invested in, and then you get paid out,

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Speaker 1: Like you just want to get paid out, and there

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Speaker 1: are a lot of opportunities to get paid out. So

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Speaker 1: that's why getting in early is a big deal. But

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Speaker 1: we start to see a rapid ascension in visibility as

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Speaker 1: media begins to report on it. Folks are talking about it,

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Speaker 1: and ultimately the text hits kind of a saturation point

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Speaker 1: for awareness, and the tech then is said to be

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Speaker 1: at phase two, which is the peak of inflated expectations.

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Speaker 1: It's where people are the most most excited for this

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Speaker 1: new technology and speculation is running wild about how this

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Speaker 1: tech is going to change everything. And it's at this

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Speaker 1: stage where people expect this new technology to do things

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Speaker 1: it simply will not be able to do. Maybe one

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Speaker 1: day it can do some version of that thing, but

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Speaker 1: it certainly can't do it right away. So with AI,

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Speaker 1: you could say the belief that AI is ready to

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Speaker 1: transform business across every industry and produce instantaneous results, that

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Speaker 1: would be the peak of inflated expectations. It's not that

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Speaker 1: the tech may not ultimately get to that point, but

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Speaker 1: it can't do it now. Businesses are still grappling with

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Speaker 1: how to integrate AI in ways that make sense to

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Speaker 1: their workflow and operations. To use an analogy, if I

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Speaker 1: were a hiring manager and if I hired on a

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Speaker 1: brand new employee, a human being, and this human being

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Speaker 1: is incredibly skilled, and they're knowledgeable, and they have an

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Speaker 1: amazing work ethic, the hiring manager I would expect this

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Speaker 1: employee to make significant contributions to corporate success. Down the line.

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Speaker 1: But I wouldn't think that they would just transform the

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Speaker 1: organization overnight unless this was like some weird production of

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Speaker 1: the musical How to Succeed in Business Without Really Trying

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Speaker 1: or something. Yeah, if it's a fantasy film or something

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Speaker 1: like that, sure, but in the real world, no, even

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Speaker 1: the best higher in the world is it going to

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Speaker 1: transform the business overnight. It's going to take time. Well,

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Speaker 1: we should hold AI to the same set of standards.

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Speaker 1: You know, it's not something that's just magically going to

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Speaker 1: transform everything. Okay, that's just the first two phases of

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Speaker 1: the hype cycle. We've got three more to get through.

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Speaker 1: But before we do that, let's take a quick break

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Speaker 1: to thank our sponsors. Okay, we're back. So the world

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Speaker 1: realizes that the hyped technology from the first two phases

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Speaker 1: cannot meet the peak of inflated expectations, and folks start

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Speaker 1: to get disappointed and excitement and enthusiasm begins to drain away. So,

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Speaker 1: according to the hype cycle, visibility now goes into decline.

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Speaker 1: People move on to do other things. Some folks will

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Speaker 1: stick with the technology typically, I mean it's rare that

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Speaker 1: everyone abandoned ship. If they did, then that would just

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Speaker 1: be the end of that story, right. The technology just

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Speaker 1: wouldn't have any support. No one would be putting any

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Speaker 1: money into R and D. It just it'd be a

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Speaker 1: dead end most of the time. Though there are those

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Speaker 1: who stick with it, especially those who never really hyped

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Speaker 1: up what it was capable of in the first place.

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Speaker 1: They had more measured expectations. The initial ground swell of support, however,

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Speaker 1: is gone. The tech then moves into the troth of disillusionment.

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Speaker 1: So in this phase you see a lot of sad

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Speaker 1: stuff happen because startups that launched during the earlier hype

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Speaker 1: phase face a really tough reality. Unless they can convince

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Speaker 1: stakeholders to hang on, they may find themselves out of

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Speaker 1: investment money and they just fade away. So a lot

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Speaker 1: of startups fail during this phase if you subscribe to

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Speaker 1: the Gartner hype cycle model. So this really did happen

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Speaker 1: with a lot of VR companies back in the nineteen nineties.

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Speaker 1: I'll talk more about VR a little bit later in

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Speaker 1: this episode, but we actually saw this kind of play out.

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Speaker 1: In fact, you could argue VR was one of those

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Speaker 1: technologies that inspired the creation of the hype cycle in

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Speaker 1: the beginning anyway, but even larger companies that existed before

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Speaker 1: the tech had hit the scene may find themselves in

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Speaker 1: trouble if they invested too heavily in whatever the technology was.

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Speaker 1: So with AI companies like Intel, Meta, Google, and Microsoft,

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Speaker 1: they kind of fall into this category like they're huge,

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Speaker 1: and they're much bigger than just artific intelligence. That's not

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Speaker 1: the only business they're in, right, It's not like they're

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Speaker 1: a startup that's totally focused on AI, but they've also

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Speaker 1: spent an enormous amount of money in AI research, and

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Speaker 1: for interest and enthusiasm around AI to kind of fade

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Speaker 1: is bad news for them because that's one less thing

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Speaker 1: they can hype up to their investors when it comes

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Speaker 1: to things like earnings, calls, and such to get them

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Speaker 1: excited and reinvest in the company. Gradually, according to the

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Speaker 1: Gartner hype cycle, folks start to figure out the best

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Speaker 1: uses for the technology in question, whatever it may be.

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Speaker 1: These uses might not be as transformative and impactful as

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Speaker 1: what people believed, especially at the height of the peak

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Speaker 1: of inflated expectations, but assuming the technology has any utility

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Speaker 1: and value to it, enough folks will stick with it.

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Speaker 1: It'll find its place and gradually and with much less hoopla,

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Speaker 1: folks will adopt this technology. This Gartner calls the slope

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Speaker 1: of enlightenment. People take a more grounded approach to implementing

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Speaker 1: this tech. The slope of enlightenment then leads to the

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Speaker 1: final phase in the Gartner hype cycle, called the plateau

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Speaker 1: of productivity, where folks and organizations make regular use of

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Speaker 1: this technology in ways that just makes sense. And this

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Speaker 1: technology might not boost company profits into overdrive, but they

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Speaker 1: would improve results in various ways. That's the hype cycle,

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Speaker 1: which again is not a cycle. Now, as I mentioned earlier,

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Speaker 1: this observation is really just that it's not a law,

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Speaker 1: and some argue it's not even an accurate observation that

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Speaker 1: technologies don't necessarily follow this pattern that Gartner has laid out.

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Speaker 1: Some may argue that it's more accurate to say it's

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Speaker 1: the marketing around technologies that follow at least a variation

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Speaker 1: of this story, but that the tech itself really should

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Speaker 1: be considered separately. Also, the terminology used in the Gartner

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Speaker 1: hype cycle gets pretty wishy washy because it's not quantitative.

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Speaker 1: You cannot measure it right. Inflated expectations, disillusionment, enlightenment, these

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Speaker 1: words have no objective meaning or means of measurement when

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Speaker 1: it comes to a technology's success or even acceptance. Also,

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Speaker 1: it's pretty hard or even impossible, to tell where any

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Speaker 1: technology might be along this cycle until it moves on

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Speaker 1: to the next phase. Right, how can you say, oh,

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Speaker 1: we're at the peak if then next week it goes

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Speaker 1: even higher like oh, I was wrong, Now we're at

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Speaker 1: the peak, And you might be doing that over and

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Speaker 1: over again. And as I mentioned before, another really big

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Speaker 1: issue is that this doesn't identify anything that actually causes

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Speaker 1: the technology to transition from one phase to the next. So,

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Speaker 1: at least according to some critics, not only can you

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Speaker 1: not tell where along this supposed hype cycle technologies may fall,

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Speaker 1: you don't know when they're going to make the turn

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Speaker 1: to the next stage, and you don't know how dramatic

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Speaker 1: that next stage is going to be. It's possible that

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Speaker 1: the trough of disillusionment isn't as big a dip, or

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Speaker 1: it could be a much worse depth like NFTs, I

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Speaker 1: would argue, went through a really big one. After they

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Speaker 1: hit a really tall peak, they went to a really

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Speaker 1: deep trough. And it's not like NFTs don't exist now

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Speaker 1: but boy, they are not anywhere close to the level

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Speaker 1: of popular that they were at the height of the

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Speaker 1: NFT frenzy. You could also argue some technologies don't seem

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Speaker 1: to follow this path at all. I think consumer smartphones

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Speaker 1: fall into this camp, Like when Apple launched the iPhone.

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Speaker 1: Smartphones had existed before the iPhone. Apple is known as

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Speaker 1: a company that launches refined products, as in products that

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Speaker 1: have already been on the market in some other form,

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Speaker 1: but Apple has put its own refinement on that technology,

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Speaker 1: and that's where a ton of the value comes from.

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Speaker 1: So when Apple launched the iPhone, even though the iPhone

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Speaker 1: was not the first smartphone to ever hit the market,

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Speaker 1: it was the first one that I think was really

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Speaker 1: marketed to the average consumer as opposed to executives and

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Speaker 1: early adopters. Well, I think the hype just went up

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Speaker 1: and up and up with the iPhone and with each

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Speaker 1: subsequent iPhone release for quite some time, until we reached

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Speaker 1: a point where the improvements from generation to generation were

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Speaker 1: bound to be incremental rather than revolutionary, which was gonna happen. Right.

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Speaker 1: You can't have every phone reinvent the phone every single time.

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Speaker 1: It might work for the first few generations simply because

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Speaker 1: of the rapid development of technology. But you eventually start

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Speaker 1: getting diminishing returns, and so then you get the incremental improvements.

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Speaker 1: But I don't think we ever really entered a trough

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Speaker 1: of disillusionment with consumer smartphones. I don't think that happened.

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Speaker 1: I think people just sort of entered into like a

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Speaker 1: realm of managed expectations. For the most part, you still

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Speaker 1: see people get excited every fall, hoping that the next

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Speaker 1: Apple iPhone event is going to be one that blows

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Speaker 1: us all out of the water by introducing some feature

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Speaker 1: no one ever even imagined, which is an unrealistic expectation.

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Speaker 1: It may still happen on occasion, but it's unrealistic. So

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Speaker 1: I don't think consumer smartphones followed the hype cycle, so

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Speaker 1: not every technology goes along this path. Still, While I

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Speaker 1: share skepticism that the hype cycle is the be all,

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Speaker 1: end all description of the phases in technological acceptance and adoption,

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Speaker 1: I do think it provides a useful starting point for

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Speaker 1: discussions around technology that have experienced a skyrocketing early effect

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Speaker 1: followed by a quick dip once folks realize that the

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Speaker 1: tech perhaps is not fully baked. So I already mentioned

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Speaker 1: VR virtual reality as an example of that. In the

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Speaker 1: late nineteen eighties, virtual reality was just starting to get attention.

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Speaker 1: You know, Jaron Lanier, who is typically credited as the

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Speaker 1: person who coined the term virtual reality, did that somewhere

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Speaker 1: around nineteen eighty seven, so it's a pretty recent tech,

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Speaker 1: and the concept from the get go was intriguing. Instead

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Speaker 1: of staring at a computer screen and interacting through mouse

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Speaker 1: and keyboard, the future of computing would put you inside

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Speaker 1: the programs. It was like Tron, but for real. Okay,

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Speaker 1: for people who aren't old. The original Tron film came

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Speaker 1: out way back in nineteen eighty two, and it featured

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Speaker 1: a human character getting digitized and uploaded into a computer

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Speaker 1: as a program, so he was inside the computer. That's

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Speaker 1: kind of how people thought of virtual reality in the

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Speaker 1: early days. So imaginations ran wild with the idea of VR.

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Speaker 1: You'd be able to navigate operating systems and programs the

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Speaker 1: same way as you would walk around like a building

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Speaker 1: or even a city landscape. Never mind that that isn't

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Speaker 1: really efficient or practical. The concept of that really appealed

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Speaker 1: to people, and you can see reflections of this to

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Speaker 1: this day and other ideas like the metaverse. You still

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Speaker 1: have people holding on to this idea that somehow navigating

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Speaker 1: computer programs as if they were physical landmarks is appealing.

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Speaker 1: I'm no longer convinced that it is. But anyway, you'd

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Speaker 1: be put into the middle of all this stuff, whether

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Speaker 1: it was to blast polygonal aliens or perform surgery on

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Speaker 1: a patient who's half a world away. The sky was

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Speaker 1: the limit with virtual reality. There was a ton of

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Speaker 1: money thrown at VR in those days. R and D

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Speaker 1: departments were flush with cash, and a lot of folks

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Speaker 1: started doing really cool research in VR. But all was

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Speaker 1: not well. A few companies rushed to develop consumer VR experiences.

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Speaker 1: That equipment was far too large and expensive for anyone

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Speaker 1: other than the very wealthy to own it for themselves,

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Speaker 1: so the idea wasn't to create consumer products. Instead, the

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Speaker 1: idea was to create VR arcade experiences. So you had

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Speaker 1: companies like Virtuality building these enormous gaming rigs that included

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Speaker 1: bulky head mounted displays and pedestals with a railing built

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Speaker 1: in so that it would prevent players from stepping off

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Speaker 1: and falling over. Arcade operators would charge players to play games.

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Speaker 1: Sometimes it was a flat fee for a specific game title.

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Speaker 1: Sometimes you were paying for like five minutes of game

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Speaker 1: time per go, which seemed expensive, but then if you

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Speaker 1: spent five minutes in VR, you'd be like, no, I'm good.

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Speaker 1: But players who took the plunge like yours truly. I

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Speaker 1: was one of these people. There was a mall. Doesn't

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Speaker 1: it no longer really exists. I mean the building does,

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Speaker 1: but there's hardly any businesses in it. But when that

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Speaker 1: place mall was the big mall that was within an

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Speaker 1: hour's drive of where I grew up, and we would

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Speaker 1: go there on occasion, and they had a VR arcade

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Speaker 1: in that mall, and people who tried it out, including myself,

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Speaker 1: often we would be impressed with the user interface experience

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Speaker 1: because being able to look around and change your point

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Speaker 1: of view in game by physically moving your body, that

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Speaker 1: was a really big deal. You know, first person shooters

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Speaker 1: weren't really a genre yet when these VR systems first

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Speaker 1: hit the market in like nineteen ninety one. Wolfenstein three D,

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Speaker 1: which really set the stage for first person shooters, came

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Speaker 1: out in nineteen ninety two, and you couldn't even really

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Speaker 1: aim up or down in that just kind of left

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Speaker 1: and right, So being able to move around a virtual

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Speaker 1: environment and to control your perspective just by turning your

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Speaker 1: head or squatting down or whatever, that was a big deal.

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Speaker 1: But the graphics were really primitive, and that's putting it lightly.

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Speaker 1: They were blocky, and on screen characters typically had only

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Speaker 1: a few points of articulation. This was necessary at the

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Speaker 1: time because of the massive amount of processing power needed

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Speaker 1: to make everything work. By massive, by the way, I

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Speaker 1: mean relative to the capabilities of the time. Today, the

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Speaker 1: requirements of a nineteen ninety one era VR game would

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Speaker 1: be trivial. You could probably run it on your phone,

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Speaker 1: but back in nineteen ninety one it required a lot.

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Speaker 1: As folks realized the limitations of VA are at this stage,

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Speaker 1: excitement kind of puffed out of existence. So there had

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Speaker 1: been all this media hype, especially like in movies, where

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Speaker 1: the concept of VR became like an integral part of

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Speaker 1: the plot of films, and then people realized, oh, that's

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Speaker 1: not actually where VR is. It's nothing close to what

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Speaker 1: we've been thinking about. And with excitement and enthusiasm fading away,

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Speaker 1: the money followed, so research labs that had been enjoying

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Speaker 1: support suddenly were scrambling to stay afloat, and a lot

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Speaker 1: of companies and labs would either go out of business

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Speaker 1: or they had to pivot to something else. Folks who

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Speaker 1: were determined to use VR to tackle issues like treating

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Speaker 1: mental issues like I know ones that were used for

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Speaker 1: psychology purposes and used to treat things like phobias, You

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Speaker 1: would use VR to do kind of an immersion therapy

432
00:25:53,040 --> 00:25:56,040
Speaker 1: where the trigger the person was afraid of could be

433
00:25:56,040 --> 00:26:00,480
Speaker 1: introduced virtually and the person would still have the reaction

434
00:26:01,080 --> 00:26:03,960
Speaker 1: to whatever their fear was of, but they would know

435
00:26:04,000 --> 00:26:06,399
Speaker 1: that they were in a safe space ultimately, and it

436
00:26:06,480 --> 00:26:09,840
Speaker 1: was a way to have immersion therapy without a real

437
00:26:10,000 --> 00:26:14,719
Speaker 1: danger being present or a perceived danger actually being present.

438
00:26:15,480 --> 00:26:18,879
Speaker 1: And it was an incredibly interesting area of research, but

439
00:26:19,440 --> 00:26:22,240
Speaker 1: the money started to go away. Folks who were determined

440
00:26:22,280 --> 00:26:26,360
Speaker 1: to keep using VR had to scrape for every penny

441
00:26:26,520 --> 00:26:29,040
Speaker 1: and would often have to repurpose gear that was made

442
00:26:29,119 --> 00:26:32,520
Speaker 1: for other purposes, primarily stuff like gaming systems, in order

443
00:26:32,560 --> 00:26:35,080
Speaker 1: to keep going. And it would stay like that until

444
00:26:35,080 --> 00:26:38,440
Speaker 1: the two thousands, when VR would experience a more measured

445
00:26:38,520 --> 00:26:42,720
Speaker 1: amount of support. Now, the VR story gets more complicated

446
00:26:43,160 --> 00:26:46,880
Speaker 1: and it doesn't really fit the hype cycle format. Easily

447
00:26:46,960 --> 00:26:50,520
Speaker 1: once we get past that initial part of the story

448
00:26:50,560 --> 00:26:54,280
Speaker 1: of the peak of inflated expectations in the trough of disillusionment.

449
00:26:54,640 --> 00:26:57,000
Speaker 1: I'll talk more about that in just a moment, but

450
00:26:57,119 --> 00:27:09,879
Speaker 1: first let's take another quick break. Okay, before the break,

451
00:27:09,880 --> 00:27:13,840
Speaker 1: I said the VR story doesn't fully fit the hype cycle,

452
00:27:14,240 --> 00:27:17,720
Speaker 1: at least not perfectly. The initial part of it seems

453
00:27:17,720 --> 00:27:21,480
Speaker 1: to quite well, right. VR as a concept starts to

454
00:27:22,280 --> 00:27:26,240
Speaker 1: break into mainstream consciousness after being kind of the realm

455
00:27:26,320 --> 00:27:31,840
Speaker 1: of engineers and computer scientists and various technical conferences, and

456
00:27:31,880 --> 00:27:35,199
Speaker 1: then people get really excited, Hollywood gets really excited, and

457
00:27:35,240 --> 00:27:37,800
Speaker 1: then folks get to experience it, and that excitement goes

458
00:27:37,840 --> 00:27:41,840
Speaker 1: away and the investment goes away, and VR nearly died

459
00:27:41,960 --> 00:27:44,800
Speaker 1: as a result. But despite the fact that you had

460
00:27:44,800 --> 00:27:47,359
Speaker 1: a lot of hype and you had this rug pole

461
00:27:47,480 --> 00:27:52,320
Speaker 1: moment where support rapidly disappeared, the technology did build its

462
00:27:52,359 --> 00:27:55,920
Speaker 1: way up again, kind of like that slope of enlightenment

463
00:27:56,000 --> 00:27:59,000
Speaker 1: story with the hype cycle. But VR's relationship with other

464
00:27:59,040 --> 00:28:03,440
Speaker 1: technologies like mixed reality, which brings augmented reality into it,

465
00:28:03,760 --> 00:28:08,000
Speaker 1: that makes VR too complex for the cycle to accommodate,

466
00:28:08,280 --> 00:28:10,840
Speaker 1: because it's not like it's a single story, it's part

467
00:28:10,840 --> 00:28:15,840
Speaker 1: of a multi branch story. It's most things in our

468
00:28:15,920 --> 00:28:19,399
Speaker 1: lives fall into this, right I like to think I

469
00:28:19,400 --> 00:28:22,320
Speaker 1: shouldn't say we. I like to think of history as

470
00:28:22,359 --> 00:28:25,359
Speaker 1: a series of stories, But in reality things are so

471
00:28:25,440 --> 00:28:29,000
Speaker 1: complicated you rarely ever have a true beginning, middle, and end,

472
00:28:29,480 --> 00:28:32,320
Speaker 1: which is unfortunate for people like me who really like

473
00:28:32,400 --> 00:28:36,120
Speaker 1: to have that kind of structure and closure. But that's

474
00:28:36,160 --> 00:28:39,280
Speaker 1: not how reality works. Well that's not how VR works either.

475
00:28:39,440 --> 00:28:44,160
Speaker 1: VR is deeply integrated into other disciplines, and other disciplines

476
00:28:44,200 --> 00:28:47,160
Speaker 1: are a big part of VR. So you can't really

477
00:28:47,160 --> 00:28:50,160
Speaker 1: talk about VR as a specific technology, right It depends

478
00:28:50,240 --> 00:28:52,480
Speaker 1: upon a lot of other technologies, many of which are

479
00:28:52,640 --> 00:28:56,680
Speaker 1: are far more mature than VR is, and these technologies

480
00:28:56,680 --> 00:28:59,560
Speaker 1: have proven themselves, so it is too complicated to just

481
00:28:59,680 --> 00:29:03,400
Speaker 1: reduce down to VR is a technology. So critics could

482
00:29:03,440 --> 00:29:06,960
Speaker 1: argue that the hype cycle is fine for contextualizing the

483
00:29:06,960 --> 00:29:11,400
Speaker 1: initial era of VR, but that doesn't really hold up

484
00:29:11,440 --> 00:29:14,080
Speaker 1: once you get past that. And the same might be

485
00:29:14,240 --> 00:29:20,080
Speaker 1: true for artificial intelligence. Certainly, AI is not a single technology,

486
00:29:20,600 --> 00:29:24,000
Speaker 1: right like we often talk about it. Even I often

487
00:29:24,040 --> 00:29:27,080
Speaker 1: talk about AI as if it were just a single

488
00:29:27,120 --> 00:29:30,520
Speaker 1: tech like you could go into a big box store

489
00:29:30,560 --> 00:29:35,280
Speaker 1: and buy a package of AI. That's not accurate, it's

490
00:29:35,320 --> 00:29:41,600
Speaker 1: not realistic. Often it's because we oversimplify in an effort

491
00:29:41,680 --> 00:29:46,760
Speaker 1: to try and tackle a really complicated and diverse discipline.

492
00:29:47,120 --> 00:29:49,520
Speaker 1: That's what AI is. It's a discipline, and in fact,

493
00:29:49,520 --> 00:29:53,680
Speaker 1: many other disciplines feed into or overlap with AI. And

494
00:29:53,680 --> 00:29:56,480
Speaker 1: there are a lot of different implementations for artificial intelligence.

495
00:29:56,840 --> 00:29:59,200
Speaker 1: So generative AI gets a lot of the attention right

496
00:29:59,200 --> 00:30:02,920
Speaker 1: now because it's flashy and it's easier to demonstrate than

497
00:30:02,960 --> 00:30:05,560
Speaker 1: a lot of other AI applications. To the average person,

498
00:30:06,240 --> 00:30:10,239
Speaker 1: your typical human being can easily get a grip on

499
00:30:10,280 --> 00:30:13,080
Speaker 1: what generative AI is all about. They can play with

500
00:30:13,120 --> 00:30:16,600
Speaker 1: it online. You can have conversations with chadbots, you can

501
00:30:17,160 --> 00:30:21,240
Speaker 1: have an AI artist generate an image based upon your prompts.

502
00:30:21,720 --> 00:30:25,280
Speaker 1: You can even have AI generated video and music. And

503
00:30:25,320 --> 00:30:28,040
Speaker 1: I'm sure you've all seen cases where someone seemed to

504
00:30:28,080 --> 00:30:33,920
Speaker 1: equate generative AI with all artificial intelligence technologies, but that's

505
00:30:33,960 --> 00:30:38,280
Speaker 1: extremely reductive. To say that generative AI is the whole

506
00:30:38,400 --> 00:30:41,800
Speaker 1: of artificial intelligence is just wrong. There are tons of

507
00:30:41,840 --> 00:30:48,320
Speaker 1: different implementations and applications of artificial intelligence, so and many

508
00:30:48,320 --> 00:30:50,960
Speaker 1: of them have nothing to do with generating any kind

509
00:30:51,000 --> 00:30:54,000
Speaker 1: of content the way generative AI does. Now. I can't

510
00:30:54,040 --> 00:30:57,520
Speaker 1: speak for all tech communicators, but personally I found the

511
00:30:57,600 --> 00:31:02,840
Speaker 1: hype around AI this most recent round because again, AI also,

512
00:31:03,080 --> 00:31:05,800
Speaker 1: it's a discipline that's been around for more than half

513
00:31:05,800 --> 00:31:08,800
Speaker 1: a century at this point, so to call it like

514
00:31:09,080 --> 00:31:13,320
Speaker 1: new is crazy. It's been around for longer than I've

515
00:31:13,320 --> 00:31:17,200
Speaker 1: been alive. But I guess if you're looking specifically at

516
00:31:17,280 --> 00:31:20,160
Speaker 1: generative AI, even though that's been around for quite a

517
00:31:20,160 --> 00:31:24,440
Speaker 1: long time too, the most recent focus is relatively recent.

518
00:31:24,720 --> 00:31:27,400
Speaker 1: But I found it really frustrating because it's not that

519
00:31:27,520 --> 00:31:31,880
Speaker 1: I think AI isn't useful or interesting. I do. It's

520
00:31:31,960 --> 00:31:36,960
Speaker 1: just I'm so tired of seeing AI evangelists talk about

521
00:31:37,080 --> 00:31:41,120
Speaker 1: artificial intelligence as if it's already a mature discipline capable

522
00:31:41,160 --> 00:31:45,080
Speaker 1: of instantly transforming the world. Parts of artificial intelligence, I

523
00:31:45,080 --> 00:31:48,120
Speaker 1: would argue, you are very much a mature discipline, But

524
00:31:48,320 --> 00:31:53,920
Speaker 1: they might have limited practical application for real world results.

525
00:31:53,960 --> 00:31:58,520
Speaker 1: They might be more interesting in a computer science context

526
00:31:58,560 --> 00:32:02,080
Speaker 1: than in a practical application context, which isn't to say

527
00:32:02,080 --> 00:32:05,520
Speaker 1: that they won't be incredibly important, just that you're talking

528
00:32:05,520 --> 00:32:09,960
Speaker 1: about foundational building blocks that are going to be used

529
00:32:09,960 --> 00:32:15,000
Speaker 1: to build the next really cool implementation. But I'm really

530
00:32:15,040 --> 00:32:19,000
Speaker 1: tired of companies rushing into AI implementations without actually considering

531
00:32:19,120 --> 00:32:24,760
Speaker 1: what value, if any, those implementations add, and how best

532
00:32:24,800 --> 00:32:28,520
Speaker 1: to integrate them so that they actually enhance the company's business.

533
00:32:28,840 --> 00:32:31,120
Speaker 1: In most cases, I think AI ends up being a

534
00:32:31,160 --> 00:32:34,920
Speaker 1: distraction at best and harmful at worst. Now that's not

535
00:32:34,960 --> 00:32:37,440
Speaker 1: to say there are no businesses out there doing it right.

536
00:32:37,520 --> 00:32:39,800
Speaker 1: I think there are businesses that are doing this the

537
00:32:39,960 --> 00:32:43,720
Speaker 1: right way. But doing it the right way is hard.

538
00:32:44,320 --> 00:32:46,760
Speaker 1: It takes a lot of planning, and I feel like

539
00:32:46,800 --> 00:32:49,160
Speaker 1: a lot of companies are trying to take shortcuts out

540
00:32:49,160 --> 00:32:52,720
Speaker 1: of fear of being left behind if they drag their

541
00:32:52,760 --> 00:32:56,200
Speaker 1: feet on artificial intelligence, and that does not play out

542
00:32:56,240 --> 00:32:59,480
Speaker 1: well very often. Honestly, it's really making me think of

543
00:32:59,480 --> 00:33:02,960
Speaker 1: stuff like Web three and the metaverse and NFTs to

544
00:33:03,040 --> 00:33:04,960
Speaker 1: go back to that, I think all three of those

545
00:33:05,000 --> 00:33:07,960
Speaker 1: concepts have already gone through at least one round of

546
00:33:08,000 --> 00:33:10,800
Speaker 1: the trough of disillusionment. If we were to apply the

547
00:33:11,320 --> 00:33:15,400
Speaker 1: Gartner model here NFTs, I would argue you had the

548
00:33:15,400 --> 00:33:19,760
Speaker 1: most spectacular fall as unlike the metaverse or Web three

549
00:33:20,280 --> 00:33:23,520
Speaker 1: NFTs were actually a thing and you could implement them.

550
00:33:23,720 --> 00:33:27,080
Speaker 1: People are still arguing about what Web three or metaverse

551
00:33:27,200 --> 00:33:30,760
Speaker 1: even means or what they will look like when fully realized.

552
00:33:30,960 --> 00:33:35,160
Speaker 1: But NFTs existed, they were inflated like crazy, you know,

553
00:33:35,240 --> 00:33:38,160
Speaker 1: hype reached a frenzy, and then the bottom dropped out

554
00:33:38,200 --> 00:33:40,760
Speaker 1: when people seemingly woke up and said, what the hell

555
00:33:40,800 --> 00:33:43,360
Speaker 1: are we doing for the metaverse and Web three. The

556
00:33:43,400 --> 00:33:46,080
Speaker 1: decline in the excitement has meant that companies that are

557
00:33:46,080 --> 00:33:50,120
Speaker 1: determined to work on those projects are doing so an

558
00:33:50,160 --> 00:33:55,880
Speaker 1: increasingly skeptical and sometimes outright hostile environment. Meta has spent

559
00:33:56,280 --> 00:33:59,960
Speaker 1: billions of dollars in the area of developing the metafor

560
00:34:00,440 --> 00:34:03,120
Speaker 1: and investors have made it no secret that they are

561
00:34:03,200 --> 00:34:05,320
Speaker 1: not convinced that the metaverse is going to be the

562
00:34:05,320 --> 00:34:08,680
Speaker 1: next big thing. But since the digital ads business has

563
00:34:08,760 --> 00:34:12,000
Speaker 1: recently been doing much better, I think investors are willing

564
00:34:12,040 --> 00:34:14,279
Speaker 1: to look the other way with Meta. I imagine a

565
00:34:14,320 --> 00:34:16,160
Speaker 1: lot of them wish Meta would just stop with the

566
00:34:16,160 --> 00:34:19,240
Speaker 1: whole metaverse thing, but as long as it's not actually

567
00:34:19,400 --> 00:34:23,480
Speaker 1: harming the bottom line too much, then they're okay with it.

568
00:34:23,760 --> 00:34:26,840
Speaker 1: But maybe Meta's long term plan will actually pay off,

569
00:34:27,160 --> 00:34:30,880
Speaker 1: because goodness knows, I criticize companies for sacrificing the long

570
00:34:31,000 --> 00:34:34,320
Speaker 1: term in favor of short term results. So if Meta

571
00:34:34,480 --> 00:34:38,040
Speaker 1: is able to create something that people actually want to use,

572
00:34:38,680 --> 00:34:41,880
Speaker 1: then I think in retrospect people will say that Meta's

573
00:34:41,920 --> 00:34:44,600
Speaker 1: investment was worthwhile, despite the fact that it made a

574
00:34:44,600 --> 00:34:47,800
Speaker 1: lot of investors ant see. They'll say Meta was visionary

575
00:34:47,880 --> 00:34:50,439
Speaker 1: and the investors were short sighted. And you can say

576
00:34:50,440 --> 00:34:53,200
Speaker 1: that in retrospect. Right now, it's a lot harder to

577
00:34:53,239 --> 00:34:56,080
Speaker 1: say that for sure. We don't know if the metaverse

578
00:34:56,719 --> 00:34:59,319
Speaker 1: gambit is going to ultimately pay off, or if it's

579
00:34:59,320 --> 00:35:01,680
Speaker 1: just going to end up being something that ends up

580
00:35:01,680 --> 00:35:05,560
Speaker 1: being a curiosity that spent costs billions of dollars. Well,

581
00:35:05,560 --> 00:35:07,480
Speaker 1: I think the same thing could be true for AI.

582
00:35:07,800 --> 00:35:10,279
Speaker 1: I think that in the long run, AI is going

583
00:35:10,320 --> 00:35:13,879
Speaker 1: to be incredibly helpful in many ways, but it may

584
00:35:13,920 --> 00:35:18,160
Speaker 1: not be as transformative as folks were saying earlier this

585
00:35:18,280 --> 00:35:20,839
Speaker 1: year and last year, and it may require a lot

586
00:35:20,920 --> 00:35:25,920
Speaker 1: more customization to optimize AI applications for specific companies and functions.

587
00:35:26,239 --> 00:35:29,279
Speaker 1: In other words, it might not be so easy as

588
00:35:29,360 --> 00:35:33,319
Speaker 1: just throwing the AI switch and letting the money flood in.

589
00:35:33,320 --> 00:35:35,600
Speaker 1: In the meantime, it looks like we'll be in for

590
00:35:35,680 --> 00:35:38,920
Speaker 1: more rough waters. With the fears about the economy, I

591
00:35:38,960 --> 00:35:43,879
Speaker 1: personally worry about more industry layoffs across tech. We've seen

592
00:35:44,120 --> 00:35:47,840
Speaker 1: so many already in the past couple of years. Intel

593
00:35:48,000 --> 00:35:50,480
Speaker 1: recently announcing that it's going to let go of thousands

594
00:35:50,520 --> 00:35:54,560
Speaker 1: of people, like fifteen to nineteen thousand employees over the

595
00:35:54,560 --> 00:35:57,480
Speaker 1: course of layoffs in the very near future. We see

596
00:35:57,520 --> 00:35:59,840
Speaker 1: it all over the place in the video game industry,

597
00:36:00,080 --> 00:36:04,279
Speaker 1: with some entire studios getting shut down. If the tech

598
00:36:04,320 --> 00:36:08,640
Speaker 1: companies see their stock prices tumble, and that is what's happening,

599
00:36:09,120 --> 00:36:11,560
Speaker 1: we could be in for more of the same across

600
00:36:11,680 --> 00:36:14,799
Speaker 1: multiple companies. I would be shocked if we don't have

601
00:36:15,280 --> 00:36:17,719
Speaker 1: a lot more stories in the back half of this

602
00:36:17,880 --> 00:36:23,200
Speaker 1: year about layoffs and more cutbacks. And I think in

603
00:36:23,280 --> 00:36:29,640
Speaker 1: large part it's because of this diminished enthusiasm and faith

604
00:36:29,920 --> 00:36:33,279
Speaker 1: in the tech industry in general and the economy in

605
00:36:33,320 --> 00:36:36,399
Speaker 1: general as well. And that's terrible news. I really hope

606
00:36:36,440 --> 00:36:39,680
Speaker 1: anyone who's affected by layoffs lands on their feet very quickly.

607
00:36:40,200 --> 00:36:43,560
Speaker 1: It stinks. I've been there. It is a terrible feeling

608
00:36:43,960 --> 00:36:48,960
Speaker 1: and it can really make you question everything, because especially

609
00:36:48,960 --> 00:36:51,640
Speaker 1: for people who have really poured a lot of their

610
00:36:52,000 --> 00:36:55,840
Speaker 1: time and effort into a job making it a career,

611
00:36:56,160 --> 00:36:58,640
Speaker 1: for that to come to an end, it can be devastating.

612
00:36:59,040 --> 00:37:01,960
Speaker 1: So my heart goes out to anyone affected by these

613
00:37:02,080 --> 00:37:06,200
Speaker 1: kinds of things, and I'm hopeful that it won't be

614
00:37:06,360 --> 00:37:11,120
Speaker 1: as bad as I fear it will be. So I

615
00:37:11,120 --> 00:37:14,200
Speaker 1: have two different I've got the Angel on one shoulder

616
00:37:14,400 --> 00:37:17,880
Speaker 1: being hopeful and the devil on the other shoulder being fearful.

617
00:37:18,360 --> 00:37:21,319
Speaker 1: And I really hope the angels win on this one.

618
00:37:22,600 --> 00:37:25,279
Speaker 1: They're taking the outfield. From what I understand, I don't know.

619
00:37:25,320 --> 00:37:28,239
Speaker 1: I never saw that movie, so I'm pretty sure that

620
00:37:28,320 --> 00:37:33,719
Speaker 1: was a baseball film. That's it for this retrospect on

621
00:37:33,840 --> 00:37:37,160
Speaker 1: the Gartner hype cycle, how it applies to artificial intelligence,

622
00:37:37,239 --> 00:37:41,239
Speaker 1: or even if it applies, and I'll be interested to

623
00:37:41,280 --> 00:37:45,560
Speaker 1: see how things play out in the near future. I

624
00:37:45,600 --> 00:37:50,840
Speaker 1: really do question whether AI is going to be cited

625
00:37:50,920 --> 00:37:55,160
Speaker 1: as a massive factor for any kind of economic uncertainty

626
00:37:55,160 --> 00:37:58,399
Speaker 1: in the tech sphere. I think it'll be a scapegoat

627
00:37:58,640 --> 00:38:00,800
Speaker 1: for some of that, because I think these are issues

628
00:38:00,840 --> 00:38:04,600
Speaker 1: that are bigger than just the disillusionment around AI. I

629
00:38:04,680 --> 00:38:09,000
Speaker 1: do think that that's contributing. I personally don't think it's

630
00:38:09,040 --> 00:38:13,239
Speaker 1: like a lynchpin feature of what we're seeing unfold now.

631
00:38:13,800 --> 00:38:17,360
Speaker 1: All right, y'all stay safe out there. I wish you

632
00:38:17,400 --> 00:38:20,480
Speaker 1: all health and happiness, and I will talk to you

633
00:38:20,520 --> 00:38:31,360
Speaker 1: again really soon. Tech Stuff is an iHeartRadio production. For

634
00:38:31,520 --> 00:38:36,359
Speaker 1: more podcasts from iHeartRadio, visit the iHeartRadio app, Apple Podcasts,

635
00:38:36,480 --> 00:38:42,200
Speaker 1: or wherever you listen to your favorite shows