HS058: Are We Pausing The Technology Cycle?

Today we debate if technology is reaching a stable phase. Greg argues that we are in a period of stability, citing resistance to change and a slowdown in advancements. Johna believes that the rise of quantum computing and AI will lead to significant disruptions. They also discuss the future of AI and quantum computing, with Johna predicting a transformative impact by 2026, while Greg suggests a slower adoption due to existing heavy investments in technology.

Transcript

Johna (00:00:00) – Welcome to Heavy Strategy. This is a show that focuses on trying to determine the right questions, not giving you the right answers. Greg and I are going to talk today about a question that many of you might be asking yourselves, which is from a technology perspective, are we in a position of change or stability? I think you’ll see that Greg and I have very different perspectives on this, and we certainly welcome yours. Before we jump into it, please, just another reminder that we love to get feedback from you. Follow up. So please go ahead and hit Packet Pushers Net. Click on follow up for you and send us your send us your question, your thoughts and if you are interested in being on the show, let us know that as well. We are not interested in any of your private information, so you don’t have to leave your name or your email address or any of that. Just let us know your thoughts and let us know if you’d like to be on the show and how to contact you if so.

Johna (00:00:51) – So with that, Greg, you have a pretty strong perspective or a strongly held perspective. I think I should say that we’re pretty much in a period of stability when it comes to technology. Can you elaborate?

Greg (00:01:06) – When we look back, say, at the period around, say, 1999, 2000, we went through a very dynamic period of innovation, like genuine innovation, where entire programming languages were obsoleted, like things like Perl passed into, you know, not not history, but stop being the future. We saw x86 CPUs dominate and begin to iterate by somewhere in the mid 2000, though, the speed boosts flattened out, storage stopped getting larger at exponential rates and started to grow in linear rates more or less. We saw networking stabilized to sort of 100 Meg and then a gigabit, and then basically stagnate right the way up until, you know, okay, you could argue sort of 2012, 2014, 2015, when what we saw was the rise of software operations, and there was a massive transition in the way that we worked and we could call it DevOps, you could call it cloud, you could call it a whole range of things.

Greg (00:02:05) – And that period of transition or change was matched with changes in the CPU. So CPUs went from being single cores running very hot, very fast. So clock rates reached the end. You know, the clock rate of a CPU reached its end. Now we have CPUs coming out with 6428, 256 cores on board. There’s a transition there in architecture, not so much in underlying performance. In networking, we saw the transition from one gig to ten gig to 25 gig to 100 gig in storage. We saw the transition from spinning rust to SSDs and now NVMe and so forth, and somewhere I think in the last two years, we’ve seen a position where customers are saying or the market is now saying, okay, we’ve just we’ve got enough. I’m not willing to embrace any more change. And there’s been this dramatic period of growth. And I think that the stagnation actually started just before Covid came in, and then Covid came along and it became or technology or the use of technology to change businesses during the Covid period created a sort of a false period or a false dawn that in another innovation cycle was starting.

Greg (00:03:18) – And then I think what we’ve seen today is no, there is no next generation, because if you look back over time, what would happen is we’d have these periods of do you know much about the theory of punctuated equilibrium?

Johna (00:03:31) – I do well, no, I don’t know much about it. So. Right.

Greg (00:03:35) – So punctuated equilibrium is the theory that evolution is not linear. And it’s kind of it’s generally an accepted theory that’s you can’t write it down to mathematically prove it. But if you look at the data, it certainly matches the trend line. Right. Is that at certain points in the evolution of the of the planet Earth, there were punctuated equilibrium where certain points where key transitions happened and then the rest resumed a linear line of progression. So, you know, there was an event on the savanna that drove the pre humanoid people or beings off the savanna and into the tropical rainforests, and that generated a whole new line of evolution and so forth and so on. The dinosaurs were wiped out when a meteor hit the planet, and therefore a whole new line of evolution started.

Greg (00:04:21) – So if you sort of take a point, I take the view that punctuated equilibrium applies to enterprise IT or technology at large. We get these key transition points where something and it’s not always a, you know, planet ending event. It’s some inflection point like the emergence of the Apple iPhone, I think is one that would be well understood. Apple didn’t really invent anything, but they put it together in a new way. And all of a sudden handheld computing worked. Even after decades of attempting, Microsoft had several attempts at it. You know, you know, there were smartphones and Nokia was having it. And all of a sudden Apple got it. And then once that. Product hit the market. There was 2 or 3 years of really furious innovation. You know, app stores, screens, 3G, 4G, 5G. You know, the whole 2G to 3G transition was driven by the rise of smartphones, the data replacing. Remember when we used to pay for phone calls and text messages per week? Yeah, long time ago.

Greg (00:05:18) – But and so that’s an example of this punctuated equilibrium. And then once the phone sort of stabilized, there’s been very little change in the last 20 years of smartphones. There’s still a slab of glass, but they’re faster, better. You know, the progression from there on in has been fairly linear. My point is, is that in in technology generally and especially in enterprise technology, we’ve had this period of punctuated equilibrium that was stopped somewhere around 2015 2016. Software defined data centers, software operation, off prem clouds, the underlying transitions in CPUs and storage and networking, the rise of GPUs and GPUs and TPUs. And now we’re in a period of stagnation where we work to absorb it. We go back to a linear progression. That’s my theory.

Johna (00:06:03) – Okay. Well, first of all, I’m very proud of myself for letting you walk through your theory from beginning to end.

Greg (00:06:10) – Because you should be.

Johna (00:06:11) – I should be because it’s actually very important to get somebody full view on the record. I’ll point out first where we agree, and then I’ll point out where we disagree.

Johna (00:06:19) – First of all, I love the punctuated equilibrium idea, which is linear until event, linear until event. I agree completely that that’s a good rule of thumb model for the way that technology evolves in the enterprise. So we’re on board there. I also agree with pretty much everything you’ve said in the past. You know, about the past, about 99 to 2000 being absolute turmoil, stabilizing for a while. And then, you know, 2014, 2015 with virtual everything, software operations automation being a hypervisors, Docker.

Greg (00:06:50) – Exactly.

Johna (00:06:51) – All of that.

Greg (00:06:51) – Cicd continuous integration.

Johna (00:06:54) – Now now my clients are talking about dealing with multicloud. And I’m sort of sitting here going, oh, you didn’t start doing that in 2015 because that’s when it became an issue. But you know, I agree. I agree with the way of characterize things. And I think that’s an incredibly important backdrop. Finally, the last point of agreement is, you know, you couldn’t see me nodding here because this is a podcast. But when you said, look, Apple took the iPhone and it wasn’t the first mobile phone and it wasn’t even the best mobile phone, but it was the one that was easiest to use and therefore was changing the way that enterprise is used it.

Johna (00:07:27) – I think that’s absolutely key, because if you look at things like I now, I mean, I has been evolving since I was in undergrad school, so it’s not like it’s new at all. What changed was ChatGPT put the potential of AI in the hands of ordinary people, and that allowed people to think about how it could be used just the same way as we had mobile phones before the iPhone. But the iPhone changed everything because an ordinary human could sit there with an iPhone and go, oh, I could do all sorts of things at work with this tool. Why am I not doing that? However, I would argue that you’re looking with the rear view mirrors almost perfectly and not looking forward. And the two things that you really you’re really missing, looking forward is the rapid evolution of quantum computing, which is leading to all of the changes and all of the turmoil you’re talking about. All of the hardware is getting reinvented, rediscovered, redeveloped in order to support quantum computing software. Programming languages are getting changed from the ground up because you can’t program a quantum computer using classical, any classical algorithms or any classical languages, you actually have to redevelop.

Johna (00:08:31) – So there’s an enormous turmoil happening right now in applications and software. The hardware underpinnings are going into place now. Literally cutting edge organizations are buying quantum computers and installing them and beginning to focus them on long range problems. And most importantly, companies are starting to think about the interplay between AI and quantum, because I right now, it’s a series of classical algorithms that can solve that are optimized for solving certain kinds of problems. Quantum is optimized for serving, solving different problems, but people are starting to look at the way that you can bring both of them together to solve problems faster than ever before. And a great example is with the Swedes did earlier this year at the beginning of the year, where they actually cracked some of nist’s. And you and I’ve talked about this, some of some of nist’s encryption algorithms by a combination of quantum computing augmented with AI.

Greg (00:09:25) – I guess I don’t see quantum computing as a disruption point. To me, it sounds like an evolution of the computational. Oh, you’ve got to be kidding me.

Johna (00:09:33) – No, no, it takes problems that you couldn’t solve. It doesn’t do very well at solving classical problems. In fact, that’s a huge misconception that quantum is just about solving classical problems faster. The big thing about quantum is that it can give you viable solutions to things like NP hard problems. NP hard problems can’t be solved sort of, by definition, can’t be solved with classical computers. Quantum doesn’t necessarily solve them. But it gives you viable solutions. And the best way to think about what quantum allows you to do is quantum allows you to explore multiple different scenarios or solutions at the same time and play them out in parallel. Any classical algorithm requires you to do that sequentially, which means that there’s just an infinite amount of time before you can solve it. Because just let’s just say if there are ten scenarios, then classical is going to take you ten x the time. And I’m just simple way over simplifying it. But if there’s an infinite number of scenarios, classical is going to take you an infinite amount of time to get there, which means you’ll never get there.

Johna (00:10:37) – Whereas with quantum, you can be looking at as many possible scenarios as you wish and solving the problem within certain parameters, bounded parameters. So what I’m talking about is if you look at the traveling salesman problem, classical case of NP hard, you how do you hit? How do you travel the entire country or a geography and hit every single, every single city, the least number you want to hit every single city while backtracking the least number of times what quantum computing will give you is the ability to solve that problem differently. You’re leading to the.

Greg (00:11:11) – Fact that quantum will lead to.

Johna (00:11:13) – Well, let me finish. Let me finish this because this example becomes clear. If you say, I want all possible solutions that are optimal within, by a certain definition of optimal quantum will kick out a whole set of solutions. If you say, I don’t want to have to backtrack more than once, quantum will kick out a bunch of solutions, or I don’t want to have to backtrack more than three times. You may not be able to get it all the way to the end of the most optimal path ever.

Johna (00:11:39) – But as long as you’re willing to accept solutions that are reasonably optimal within a definition of optimal quantum, your quantum your tool. And let me give examples of how that might work. You might say, oh, I want a security policy, and I want the security policy that’s reasonably optimal for my organization. By this definition of optimal and quantum will play out all variants of your security policy and give you a bunch of choices with a bunch of of tradeoffs that you can make. And oh, by the way, you can maybe use AI to make some of those trade offs once you get the answers. You know, there’s interesting questions of whether you want to use AI first and quantum second or quantum first and second. In both cases it can happen. So back over to you.

Greg (00:12:20) – So I would say to you, yes, I agree that there is reason to believe that quantum will be a disruptive point potentially at some point in the future. We don’t know when we need. There’s a bunch of physical things not to have to happen around superconductors and so forth.

Johna (00:12:36) – Nope, nope, that is not true. Not true and not true. We know it. We know it is happening now. People are buying. These superconductors are not necessarily required for quantum hardware. People are coming up with all sorts of different approaches. There is a plethora of choices. It’s what it’s the wild and crazy, wild and wooly West, so to speak. In terms of looking at all the different hardware options. There are companies out there that are plowing enormous amounts of resources in this today, and there are enterprises that are already buying these now, these are leading edge organizations. But that said, you know, I expect that my research lab clients are doing this. No surprise there. I expect that the defense contractors are doing some of this. No surprise there. But what’s a little surprising is when you’ve got folks like advertising agencies doing things here and there, leading edge on this, because they see how it’s completely revolutionizing the services they’re able to offer their clients. The fact that they have stood up quantum computing inside their organizations today is a little eye opening.

Johna (00:13:37) – So when you say, oh, it’s off in the future, we’re awaiting physical developments. The reality is, and this is kind of the point that I’m making. While I agree with your punctuated equilibrium two points, it’s been it seems to happen about every decade or so, and it’s been a decade since 2014. And the other thing is, the rate of change with quantum and AI is surprising even to those of us who’ve been in the industry for a long time. It’s like the rate of change itself has increased. So I would maintain that we are actually not quite there yet, but on the absolute cusp of the most.

Greg (00:14:14) – We’re still positing a future disruption. It’s not a it’s not a button yet.

Johna (00:14:18) – Well, no, it’s a future as in six months to 12 months. So it’s like sitting there at at January 1999 and going, yeah, I’m positing a disruption.

Greg (00:14:28) – I think one of the things that we’re seeing is that a lot of customers are having a fatigue. And one of the things that I’ve noticed over the 30 odd years that I’ve been in the industry is that at some point customers go, I’m just tired of the change and I’m not interested.

Greg (00:14:43) – And I think we’re actually seeing that now as the vendors work through the backlogs from Covid. So if you read the financial reports for various vendors, they’re now reaching the end of the backlogs that accrued during the Covid period. Right? They’ve shipped all the back orders are now being fulfilled. They. Getting significant boosts to their bottom line now as those backlogs make their way out of the system. But there’s there’s a very big slump in demand in a year’s time because customers are saying, okay, well, I’ve bought all this technology, now I have to digest it. Now I have to extract value from yeah, I have to rationalize it.

Johna (00:15:16) – Yep.

Greg (00:15:17) – Now there is only one. Now, if you’re really focused on the on the new shiny shiny then AI is your shiny. It’s going to be it is out there proposing a new way to work. But my point about AI is it’s actually not disruptive because there’s a significant piece of business around generating the inference. So taking the data and inferring models from it.

Greg (00:15:40) – And then there’s a completely different value chain attached to taking the model and then applying it to the data. So you can get meaning from the data. Right. Does it. Do you agree with that?

Johna (00:15:49) – Yes and no. I agree with everything you said, except that it’s not disruptive because my contention is AI is disruptive for the same reason that the iPhone was so focused.

Greg (00:15:57) – On infrastructure here. We’re not focused.

Johna (00:15:59) – No, no, I understand, but my my point is we’ve done we’ve actually done the research. And it turns out that within enterprises line of business, use of AI exceeds exceeds the ability of it to manage it. So, so it’s very, very similar to the iPhone where you can say, well, the iPhone isn’t really disruptive because all it is is a phone. You can do email and web surf on. And we’ve had those, which is sort of true but not really true. The difference is that that there is a meaningful difference when a handful of people who are arcane technologists have something versus absolutely everybody can go and buy it from the nearest Apple Store.

Johna (00:16:36) – And that’s where we are with the AI. And we’re seeing that in enterprise organizations that lines of business have actually invested more headcount and are more likely to be using AI than it, even both at relatively high levels.

Greg (00:16:49) – And at the same time. We’ve got, you know, a fairly large group of managers. I was talking to a couple of analysts recently, and they’re saying a lot of them still haven’t gotten over the amount of damage the big data caused. And how little value is extracted from it doesn’t matter. The AI going to run Hadoop and so forth doesn’t matter.

Johna (00:17:05) – The IT people can do whatever they want, but the teams, the teams doing AI and lines of business are bigger than the teams in it. The deployment of technology is greater than it is in it. So it’s two basic choices. You know, it’s well, three lead follower, get the hell out of the way. And oh, by the way, following and get the hell out of the way is the fast path to it obsolescence.

Johna (00:17:26) – So I don’t think my colleagues in it are going to take it in the long run. Yes, big data may have caused damage, but it also kind of was a training ground because now we understand what some of the issues are. Some of the challenges are, and in fact, the companies that are best positioned to use AI as an enterprise tool have the guardrails, guardrails and frameworks put in place.

Greg (00:17:47) – Is I actually going to be a niche technology? That is, I’m going to use it for specific. Now let’s let’s assume that ChatGPT. Yeah, that the LMS and that sort of stuff is okay for general purpose stuff. The question that’s on my mind is, does I become a ubiquitous boom, ubiquitous, and cause a disruption, or is it much more of a slow gradation? So it slowly kicks in like starts? I’m not sure that AI is disruptive. Immediately I see it much more as, oh, this is useful. It’s more like Microsoft Word and Excel. These things came along.

Greg (00:18:22) – They took years and years to be adopted. The changes that they brought to businesses were transformational, but they weren’t like a nuclear bomb going off and and wiping the slate clean.

Johna (00:18:34) – You’re moving the goalposts a little bit because arguably, you know, a win.

Greg (00:18:39) – My argument.

Johna (00:18:40) – Of course. Of course. You know, back in 1999, the internet, you know, first of all, the internet had been around for a while. And sorry. Side note, I have to tell this story. Back in the early 90s, I was a reporter at a technology publication, engineer turned reporter. I, I hasten to add, my pride requires me to. But one of the things I was writing about my my editor commissioned me to go write about this, this internet thing, and I basically came back with a story. As I said, this is going to change everything. And I remember she laughed at me and she said, Jonah, that’s cute. But no, that’s the internet is just for scientists and engineers.

Johna (00:19:19) – And I was sort of young and stupid. So my response was to get mad when I should have just smiled and said watch. So here’s where I’m smiling and just saying watch. That said, you’re not wrong, because if you look at the actual history of the internet, I was writing about it in the early 1990s, and I was hanging out at the in the in the early 1990s. By then it had already been under development for 20, 20, 30 years. And it still took another ten years to really hit that moment of boom, the nuclear boom, which you you pegged to 1999, which is as good a year as any. So I would argue.

Greg (00:19:55) – Yeah, yeah, yeah.

Johna (00:19:57) – We may we may be right on the. Of something. I would also argue it’s happening much faster. It’s not going to take a decade between the invention, you know, the development of ChatGPT, which is again, ChatGPT is not necessarily a giant innovation in AI. It’s just that they handed it to users via the cloud.

Johna (00:20:14) – That’s the innovation. And it’s just like the iPhone is not a major innovation. It’s just a shiny white package that you could give to consumers. That’s the innovation. But my contention is that we’re not going to wait anything.

Greg (00:20:25) – Of existing technology.

Johna (00:20:27) – Right? We’re not going to wait another ten years for that boom explosion to go off. It might be ten months, it might be two years, but it’s happening. And so the key thing is that AI is disruptive. In my contention, AI is disruptive individually. Quantum is disruptive individually. Plus quantum is disruptive enormously because it completely changes the natures of the problems that we can solve and even the approaches that we take. And that’s you can’t get more disruptive than that, really.

Greg (00:20:57) – So whereas I disagree, I think the impact of AI and quantum will be very slow. I think we’re we’re actually now in a period of absorbing the technologies that we have, as you say, I remember being involved in the internet in the 98, 97, 98 and the then the.com boom hit us.

Greg (00:21:16) – Right? And then there.

Johna (00:21:17) – Was the post.

Greg (00:21:18) – Crash, right, which we’ve been.

Johna (00:21:19) – Living with for 20 years. I want my mind at I think we’re finally, you know, that’s the other macro change which we not really a point of contention that it has been living in the shadow of the.com crash for 20 years and has only now escaped it.

Greg (00:21:33) – I would agree with you that there are definitely we could point to things and say these could be future disruptions. I would also, but what I’m also seeing from a lot of people is saying there’s only so much we can absorb. There’s only so many things we can buy before we have to extract value from them. So you could make an argument that says, reduces the burden of taking onboarding new technologies and gives you lower cost ways to start the adoption in the old days, you used to have to spend $300 on a copy of Microsoft Windows $1,300 to get Microsoft Office. Then you needed to spend $5,000 to buy a, you know, an x86 desktop sort of thing.

Greg (00:22:10) – If you can buy us access to this on a, you know, some other low cost device and then $30 a month to license it for a while, that does reduce. And, you know, there are new reasons to believe that the rate of adoption of technology is getting faster. But equally, I think we’re coming out of the era of Covid. People are tired, especially in technology. They’re tired. The rate of transition or the rate of hype around certain technologies is leading to disillusion. I think the crypto failure has undermined a lot of people’s belief in new technology, like especially to normalize the failure of crypto to meet its promise after reaching such a hype has made people very averse.

Johna (00:22:50) – Possibly, but I think we’re talking to different.

Greg (00:22:52) – To how much that’s going to blow back as well.

Johna (00:22:54) – I think we’re talking to different people because I carefully stayed out of crypto because I you know, I looked at that and I said, I can spend an awful lot of time and energy chasing this. I don’t believe that governments, in the end, will allow currencies that undermine their power.

Johna (00:23:09) – That’s just not in the nature of governments. And the governments have the guns. So I’m basically going to cross crypto off the list of interesting things. I did dig into blockchain as a back office integration technology, which in fact many of my financial clients have used it very effectively for that. And that is the big reason that financial firms are staying on top. Well, it’s one of the big reasons that they’re staying on top of quantum, because from their perspective, all of quantum boils down to, Holy crap, am I going to have to rip out all this blockchain I just put in because it won’t work anymore, which is a very narrow perspective, but I can understand why they might care. But that said, I think we’re talking to different people, and I think it’s important to understand that there is a generational shift underway. The baby boomers are slowly but surely retiring. They’ve hit this point where they’ve they’ve put in place technology. They’re now optimizing it. They want to go out, tie a nice bow on everything for their careers and go off to the future.

Johna (00:24:01) – In fact, I was just talking with a client who had met with someone else in his organization who’s since retired, and we were just talking about all the fun things this guy has done since his last big glory moment at the company was getting them set on the path to cloud, which was very much needed. Yeah, those guys, they’re tired, they’re looking at their boats, they’re looking at their house, they’re looking at retirement. But there’s an entirely new group of people who are not burdened by the definitions of problems that used to be considered unsolvable are coming in and saying, what if we maybe we could. And so when you hang out and talk to the, you know, talk to the people, talk to the quantum people, they’re generally a decade or two younger, possibly three decades younger. They’ve been working very hard themselves to get where they are, and they have a very clear vision of of the value proposition. Yeah.

Greg (00:24:52) – And our argument to AI is there’s a lot of crypto people moved over to AI.

Greg (00:24:55) – Well also has a certain whiff of.

Johna (00:24:59) – Like, yes there is. There is a huge amount of fuzziness with I know you’re not entirely certain and I do. You know, I do distrust things when they’re getting heavily hyped, which is one of the reasons that quantum is so interesting, because people are so intimidated by it that they tend not to hype it. But the reality is, people who truly understand AI and people who truly understand quantum do understand how transformational these technologies are sometimes together. This isn’t like happening off in the far distant future. There organizations in the here and now for whom AI and quantum are top literally at the top of the list of what they’re doing and doing in 2024, 2025, 2026. So somewhere in that time period, you’re going to see those organizations pulling way ahead of everyone else. Just like back in 1999 and 2000, there were the companies that really got the internet pulling way ahead of everybody else. That’s my prediction. Yeah. Feel free to make yours. Mean.

Johna (00:25:56) – What do you.

Greg (00:25:56) – Think? I mean, I actually agree. In my career, we hit points where the market just says, that’s enough, we’re done with it. And I’ve seen the similar things around cars, housing, you know, new materials in housing. Look at the adoption of alternative energy. You know, when we first started said we’ve got to move away from coal. Then we went to gas and then we saw solar and wind, and then everybody worked out that it wasn’t going to work and that it wasn’t very cost justifiable. And then the the ramp up of solar, you know, alternative energy sources has been very slow. There was a big hype cycle at the front that never really came out because they could there was just physical limitations. And I my instinct is that I and quantum are much more slow growers instead of disruptive events. I’m not saying that the outcomes won’t be have impact. I think there will, but I suspect it’ll be much more slow.

Johna (00:26:48) – So what’s your prediction for the end of 2026 for AI and quantum? My prediction is that that you’ll be looking at a handful, there’ll be some of it.

Greg (00:26:57) – Around and we’ll see people three, two. So I guess my instinct here is that. I think that there’s just too much hype here for it to be realistically, because we’re going to see a much more slower evolution here, and that’s going to be against a backdrop. There’s been so much change up until this point. pre-COVID, companies have invested and spent so much money on technology that they haven’t extracted value from that. For them to go out and chase after AI, except as in small test projects or where a need can be clearly delineated, I don’t think it’s going to be as disruptive as people say. My instinct is. It’s going to be much slower and a much more gradual and a much more considered process, because we haven’t been getting value from our IT investments for the last 5 to 8 years.

Johna (00:27:48) – So I’d say my prediction is very similar superficially to yours, but with one big difference. I think it’s very important to distinguish between lines of business and it you may be telling the truth for the average IT department, but I will point out again that our research shows that AI is growing dramatically outside of it, and organizations will be focused on that for a while.

Johna (00:28:13) – Let’s see, here we are almost at the end of 2023. At the end of 2026, you’re going to find a probably about 20% of leading edge companies will have active initiatives in AI and quantum, and will have been deploying them in ways that differentiate them incredibly strongly from other companies who are now feeling the pressure to competitive pressure to catch up, similar to the way that, you know, in 2010, 2000, 2001, 2002, companies that had done the internet right were leaps and bounds ahead of companies that hadn’t. And people were suddenly running around going, oh my God, Amazon’s going to eat my lunch. That’s what you’re going to see. Like you did see back in 20, in 2000, you didn’t see your average. You didn’t see Walmart looking like Amazon. You saw Amazon suddenly threatening Walmart and Walmart going, Holy crap, I got to do something about this. And that’s what I anticipate. Respond, yeah, I anticipate that by 2026, we will be well into the holy crap, I’ve got to do something with not just AI and not just quantum, but plus quantum together.

Johna (00:29:17) – And guess what? The beautiful thing is, if you’re listening to this, hopefully you’ll be around in 2026, we’ll be around in 2026, and you can come back and tell us whether we were right.

Greg (00:29:26) – Well, on that note, let’s wrap up the discussion for today. We’d love to hear your feedback. Tell us what you think. If you’ve got a contribution. And we do publish follow up shows from time to time where we collect all your follow up. If we make mistakes or get errors, we’re always willing to own up to them. Strong opinions loosely held, and we’re trying to discuss things. We’re not trying to provide you with answers. We’re hoping to help you think your way through the situation that you’re in. As always, thanks very much to Jonah for joining me today. Jonah, where can people find you?

Johna (00:29:52) – Come visit us at and hit up our community. We’ve got lots of interesting communities started, including the quantum Connection, where you can come talk all things quantum. So that’s an emerging community.

Greg (00:30:05) – And you all do pop in there from time to time. If you want to get to me, I’m Greg Ferro. You can find me on Twitter. Is that theory of mine, but increasingly more on LinkedIn. Don’t forget to come on over to Packet Pushes dotnet. Check out our website for our podcast network. We have lots of other podcasts like this where we are, although we asked quality questions here and then questions the answers that we get to them over on packet push as we try to be nerdy or weird or strange. And there’s lots of technology podcasts over there. As always, thanks very much for listening and we’ll look forward to seeing you in a couple of weeks.