The 2023 Tech Layoffs: Has the Dust Settled? with Brian Singer, Matt Peters, & Aaron Bare

Today, we're having our very first panel discussion. We're joined by Brian Singer, CTO at MedZed, Matt Peters, CTO at CAI, and Aaron Bare, the best-selling author of Exponential Theory. The panel will be tackling the theme of the 2023 tech layoffs and whether or not the dust has truly settled. You're listening to Joel Beasley, Modern CTO.

(Joel Beasley at 00:00:31) The main topic, the thing that we're all talking about, stems from the episode that Brian and I had about tech layoffs several months ago. We've been going through this up and down motion with layoffs and businesses. Everybody says that this year is a strange year or a unique year in the business world. And so the primary question I have for this conversation to start is, has the dust settled with the tech layoffs? Brian, you want to jump in there since we talked about this previously?

(Brian Singer at 00:01:01) Absolutely, jump in there. I don't think so. I've got a lot of close colleagues still in the PNW, Pacific Northwest, and there seems to be a sense, with good conscience and confidence, that there's going to be another round of RIFs coming out of there. I don't think the dust is settled also because the interesting part is after the RIFs earlier this year, there was a lot of rehires that actually took place. For example, on my part, I had a number of offers extended to former Amazonians to come and join our group in Kansas City at Propio. And, you know, last minute, eleventh hour, week before they were supposed to actually join, had accepted offers and everything, they got offers to actually rejoin their team or another team in Amazon. So it's a very odd situation that's going on. And, you know, you and I touched upon it as well. There was some odd reasoning behind who was laid off and who wasn't. Some products were killed, which made a lot of sense. But then there were other areas where, you know, senior engineers, like SD3s, which are very difficult to hire and develop within Amazon, were also let go. Now, I jumped on that and I hired a bunch of them because I knew those guys and I knew they were quality people. But I don't think we're done. I don't think so at all.

(Matt Peters at 00:02:27) Yeah, this is a weird season for me. We just happen to be in the middle right now of where I just go from conference to conference to conference. And interestingly, and this is actually a product of just last and this week's conferences that I was at, I was talking to a number of organizations, and some of them were even speaking publicly about the upcoming expectation that business leaders inside the organization were going to reduce funding in a lot of IT organizations. The motivation behind it being an irrational—I would call it—sorry, I don't mean to disparage anyone else's hot take—I would call it an irrationally overoptimistic expectation of what generative AI can do in their organization. Right? So you've got business leaders saying things like, "Why would I give my IT department funding for an entire development team for this product? I'll pay for ChatGPT and one developer, and it should be fine." And, you know, there's no short answer to that, but I don't have a wildly positive answer to that either.

(Aaron Bare at 00:03:25) Yeah, I was speaking at a conference, and there was a company there that just raised $17 million, and many of their investors were in the crowd. And the CEO made a pretty unique announcement that just said, "We're not going to hire anybody with this money. We're just going to make our developers—I've got a really good dozen developers that are 10x developers right now, and I'm going to make every one of them 100x developers." Which I think, when you get—when you realize, and I think this is kind of the Twitter experiment, right? When you trim down tens of thousands of bloated engineering and, you know, you have it at Goldman Sachs in different places, I think there were some mistakes of who they cut. I think Brian made that point. It's, you know, we're just going to go through this process and we've got to hit these revenue goals because we got over-bloated because all this government money flooded into us and we were able to hire. And all of a sudden, you know, we have this team that's ineffective and they're not really towards our massive transformative purpose. So they started shrinking that down. And, you know, part of it is also there's the uncertainty of the future, I think, for CTOs and technologists. When we look to it, what is the staffing going to be like in three to five years? Well, it's unlikely that any company needs that many more engineers, but there's going to be so much innovation. And I think there's going to be so many new companies that are entering in new areas that that's what you kind of see now, is that there's also a whole new generation of people that grew up—I think of Google as an example. They really helped create Silicon Valley's ecosystem because they're kind of the older of these big five engineering companies in Silicon Valley. All those engineers grew up, some of them exited, and they all started literally thousands of different companies. And I think this layoff is going to start to see some real good innovation. And, you know, I think also the fact that the funding isn't there like it has been means that we'll actually get real good innovation, bootstrapped and kind of growing, which, you know, I think engineers in the future, the more talented, are going to want to work on smaller teams because they're going to make a bigger impact. They're going to actually get their projects completed and done. They're not going to be one of 10,000 teams at Amazon that are competing to get their projects on the thing. So I think there's definitely a transition period that we're in, and it's going to change the careers of many engineers and technologists that thought that they would be on one path. They're most likely to be disrupted just like almost every other industry has been.

(Brian Singer at 00:06:10) You both brought up a couple of good comments that I wanted to touch on. You know, Aaron, you mentioned bloat. That definitely happened. And what bloat means is lack of performance management. And there was a lot of hesitation. And I won't get into details, but one of my experiences was very non-culturally, let's just put it that way. Where, you know, we grew up in a "just do it" culture where you thought of things, you simplified, you executed, you made sure that if you had mechanisms in place to increase efficiency, you revisited the said mechanisms to make sure they were still providing value. And you would dive deep, right? You'd actually find the root cause of a problem, not solve the symptom, and plan to solve the root cause so that you could avoid those pitfalls and bottlenecks again. A lot of that disappeared. And with that came headcount to the point where it was very obvious when you would read data that there was a headcount problem with a lack of innovation in terms of efficiencies and specialization. I had the pleasure of implementing an idea that actually optimized resources and limited their depth and breadth of having to be experts so that they could actually resolve issues quicker. It worked. But it broke the dashboard because when they pulled data globally, my data was not accurate because of the way in which I had rebuilt my team and structured it. And the data was lacking integrity on the dashboards. Rather than forcibly adopt my implementation across the globe, which I did try to do on the back end, they ultimately made me pull it back because it broke the dashboards. And that is so not innovation, right? It's like, don't care about the dashboards. You're getting better efficiencies. That's the stuff we need to worry about because had they completed the global implementation of that idea, the headcount would have been flattened for several years. And, you know, the other aspect too, Matt, I think you touched on this as well, is there's a lot of smaller businesses that really do believe that generative AI and ChatGPT can replace an engineer. Not in the future—now. I'm sure both of you have actually played around with ChatGPT. It writes pretty good code, not going to lie. It's very clean, right? However, that does not replace an engineer. That is, you know, skin and bones, very little artifacts to support what you've asked it to do. Complete abandon for what your systems really do. Sure, is it a good template? Yeah. I look at it—you guys go back—I look at it as Deja when I was coding, which Google ended up buying. We went to Deja all the time because we wanted code snippets so we didn't have to reinvent the wheel. I look at ChatGPT as just being a better version of that. I don't see it replacing software engineers, at least not in the next 12 months. Let's—I mean, it's getting better by the day. You know, I absolutely agree. Yeah, it's kind of unfounded. And the easiest comparison I can make, although they're not apples to apples, is blockchain. You know, blockchain was supposed to be this revolution, right? And that you could never steal anybody's information. It's going to be foolproof. Every transaction's going to be guaranteed and encrypted and blah, blah, blah, right? And we saw how that's all gone about, and it hasn't been fully adopted as expected. I do think generative AI is a better technology because it's been worked on for, you know, 50-plus years. Everybody's been trying to do this, but throwing all your eggs in that basket to then discount the human component and not invest back in the human capital for delivering—it's a mistake. It really is. And I can say that with confidence. I hope—you know, and I can be wrong, not going to lie—but I just don't see it replacing, displacing engineers at the moment. I see it, you know, to Aaron's point, I see it actually creating more engineers because let's be honest, a system like that isn't going to just work by itself. It's going to need more and more engineers to fulfill it, right? So instead of maybe being consumers of generative AI, the engineers may turn into contributors to generative AI, and thus just shifting the paradigm from where they're contributing their technical skills.

(Matt Peters at 00:11:00) It feels to me like we've swung back to a very loud conversation that I can recall from 2015. I was at the event where one of the CEOs of one of the largest banks in the U.S. made the very public statement and planted the flag: "We have a three-year plan through RPA. We are going to eliminate 35% of our staff. Watch us go." And it absolutely did not happen. Not even a fraction of it happened. And it stuns me that the workforce culture and leadership culture—that is a recent memory—and we're still not learning from it. So it feels very much to me right now like history is going to repeat itself. And we'll see some really extraordinary stories come out of it. Like Aaron's point that, yeah, if I've got 10x developers that I want 100x, I do believe this is the tool that could do that. But I don't believe that's a tool that's going to do that for the average individual. It's only going to make already exceptional people more exceptional. I've got an exceptional guy on my team right now who's living that experience at the moment. But I don't see any customer that I work with where I could go into an entire development shop and say all of them will be notably better almost immediately with this tool. I just don't see that happening.

(Brian Singer at 00:12:07) Well said.

(Aaron Bare at 00:12:09) I think to build on that point, Matt, when I was early in my career, I was one of the—it was a Big Five consultant, calling Big Four, Big Three, I don't know, they've continued to shrink—but I was put on my first coding job and I was to build a CRM. And this was back when Lotus Notes—so I'll date myself that I don't think there's too many Lotus Notes out there—but instead of really learning to code this CRM, this customer interface, I literally just went to Lotus Notes, found some other clients, Control-C, Control-V. I was supposed to be on this project for nine months. Three days later, I bring it to the senior engineer. You know, I'm literally a newbie just learning. I just went through my boot camp, and I said, "Hey, here's this customer interface. I think I'm done." And they're like, "That was supposed to take nine months." And, you know, you could say I was using the hack of leveraging a tool that I wasn't supposed to, I guess, you know, at that time. And then all of a sudden, you know, what you realize is that the engineer starts to find issues in my code, right? So I think we're at this stage in history where engineering and CTOs and technologists are going to become more and more important because we are going to solve more problems for technology. For the longest time in history, technology has used us, and I say that with social media and all these different things. We're now at a point that we can use it, and I think that makes and empowers the technologist where in a company, they become so much more valuable. But I also think that every other role in a company is going to have to start to adopt some of these technologies or will be eliminated. Meaning, some of these generative AI tools—maybe they're not coding things, but they're solving some of their problems or thinking about attributes and things that they weren't thinking about before. There's many different ways we can leverage AI to kind of expand the enterprise. I do think the Instagram team of 12 people or whatever that sold for a billion dollars and kind of had the highest exponential curve and obviously has been one of the saviors for Facebook—I think that team kind of format is going to be more and more where people make a bigger and bigger impact on a smaller team because of that mindset of when you have people that really know what they're doing. And I've just clearly told you that I don't, and I've learned since then. But when you have people that really know what they're doing, they could use this technology to literally create mind-blowing results. What that means is that's still competition. If you just think about the Silicon Valley talent war and you think about that's in Austin now and that's in Phoenix and Denver and all these new kind of technology epicenters, it's literally finding those people that are the 100x developers. And then they're going to have to, in a way, look at the whole organization and figure out how do they actually take that organization as fast as they can to where they want to go. But you're going to have to obviously find the newbies that are in there that are thinking that they're doing something great because they learned Control-C, Control-V, understanding that there's a little bit more to it than that. And I think that's just where we're at, and it's non-technologists, for the first time, have to know technology going forward. And I think that's where, you know, to Brian's point, I think we're going to create a massive explosion of jobs. I mean, it's the same idea when we created cars—what are going to happen to all the horses and all the other things? I mean, it's like every little innovation throughout history has always been this scarcity that we're going to eliminate people. When computers came about, Bill Gates said, "We're going to put a computer on every desk." And, you know, people are like, "Well, then we won't do anything, you know?" Well, obviously, we realized that that created 100x the work that was there before. And I think AI is that same way as we're going to find new applications for it and creativity that, you know, we just don't understand yet. And I think these 100x developers are already—you know, you guys know these people as well—they're already pushing boundaries. I didn't even think that we could possibly do this with one horsepower, if you really want to compare it to the automotive analogy. It's like the output is just going to be tremendous.

(Brian Singer at 00:16:21) Aaron, you know, you said something there—yeah. And when you said CRM and you talked about creating it, there's a very large CRM example of just that, right? And that's Salesforce.

(Brian Singer at 00:16:31) Salesforce opens up all of its tools. It allows business folks to learn a technology to somewhat self-serve. There's a lot of Salesforce engineers too, right? Like, they created a behemoth that was supposed to displace—because you take a lot of the functionality or needs, for sure, from internal custom development of back-office applications and you turn that power over to admins on Salesforce. But you still need engineers to build that platform to enable those types of functions, right?

(Brian Singer at 00:17:05) It's really interesting that you said that. And the other part, too, I do see this adding to lean practices and controlling more of that bloat. It's super easy, and I'm sure each of you has been in situations where you either assumed a position with a bloated organization, or you're continuously pressured to do more and expand, right? You know, stretch that accordion as far as it'll go.

(Brian Singer at 00:17:33) When in reality, we all know that the further you stretch the accordion, the poorer the performance and productivity. And I think I see things like generative AI eliminating noise, getting rid of waste in terms of engineer days. I want my QA teams—I want them spending a decent amount of time looking for ways in which they can improve their automation switch using something like ChatGPT. I want code reviews being done. I want to eliminate human error and common mistakes that are super easy for ChatGPT to be able to point out that typically humans miss because they're so obvious and you're, like, we read misspelled words. You read miscoded syntax, right? Those are areas where I think you can actually improve. To your point, Aaron, you can improve those 10x to 100x engineers because they're gonna go and they're just gonna be like, "Oh, I hate doing this. Let me see if this will do this for me." That to me is spot on. That's where the advantage is. And, you know, the innovation, right?

(Brian Singer at 00:18:37) I started my own business out of a situation that I found myself in because I had to. I'm sure you all have been in situations where you had to get creative, maybe not for a business or financial reasons, but we all have to get creative. And that's where the engineering mindset comes in. It's like, okay, I just have to approach this now as a problem. How do I solve that problem, or how do I make this the advantage to me?

(Brian Singer at 00:19:01) And, Aaron, the last thing I note that popped up in my head as you were explaining, you summed up the book "Program or Be Programmed." It's, you know, the 10 principles or the 10 tenets of how the culture has shifted post-social media to being told or managed by technology as opposed to programming said technology for their advancements. I'm 100% with you. I see more people getting involved in technology so that they can be the programmers as opposed to being continuously programmed by their news feeds.

(Joel Beasley at 00:19:35) I got a new question for you guys. Alright, so I went around and did a very unofficial poll by just messaging different engineering leaders on LinkedIn that I know and asking them how much of their engineering population—some had 100 engineers, some had 10,000 engineers—how much of your engineering population is using something like Copilot or some assistive technology? I got back numbers that ranged from zero to, on the highest end, around 18% of their engineering population. And this was about six months ago, and I asked why.

(Joel Beasley at 00:20:13) And the most common answer I got was they didn't trust it for working on client projects that had some sort of confidentiality or something like that. So that's one thing you can respond to and one topic there. But what I'm curious about is, to that point, other people I have talked to, I tend to get two different vibes. Well, three. One, it's there. It's amazing. It's unbelievable. We love it. We use it constantly. It's saving us money today.

(Joel Beasley at 00:20:38) Two, it's cool. It's not there yet. And then the other third, maybe maybe it's even bigger than a third, but the other third is just like, it sucks. It's not there. Like, it's not happening. We're not using it. So my question for you guys is why, as humans, clearly and obviously this has some benefit. We've all played with Copilot. We've all experienced it to some degree. But why do certain people just put their head in the sand and just say no, and they won't even think about it, consider it at all? Why does that happen?

(Matt Peters at 00:21:19) Isn't a little bit of self-preservation sometimes natural? So I can appreciate, and I can be sensitive to being afraid to accept this and move forward with it if it marginalizes me, right? I can see how someone spins that narrative in their head, and then this becomes the enemy rather than the enabler. I can keep going on that topic, but I see Aaron and Brian are both nodding, so I'll let them speak too.

(Aaron Bare at 00:21:48) Yeah, I think to build on Matt's point, in my book "Exponential Theory," take journalism. You know, they very much were against digital. Now all journalism's digital or almost all journalism's digital. You know, fast forward 10 years, I worked a lot with creative ad agencies, so creative directors that were used to doing print and magazines and they're absolutely against digital. Now there's not an agency that doesn't have the strategy as led with digital.

(Aaron Bare at 00:22:21) I think this is kind of the last of that shift to even these exponential technologies that we have to—you know, AI is one of those. But it's then stacking these AI technologies—you know, AI with some other technology to say how do you disrupt an industry. There's a few innovators that are out there and they generally leave the big companies and they go and start because they have this idea. They wanna change the world. But that self-preservation that Matt was talking about, I think it's all these different industries. People were in denial of change. And I think we're so anchored, you know, and I think organizations themselves are anchored to not change. And that's even very highly innovative organizations, you know, are gonna fight off change. And it's almost like the immune system of a human body. Like, you know, when a foreign invader comes in and is gonna change anything, it's attack.

(Aaron Bare at 00:23:16) And that happens over and over again. So you don't really get the change. So what happens is most organizations get over-bloated because they're like, we're not solving these problems. Keep hiring different people. Let's bring in new people. But then they learn the culture quickly and learn how to survive by not standing out. And I think in the end, it's this transition that, you know, these thirds that you kind of shared in your thing—it's like there is this transition that the early adopters, as we know on the curve of early adoption, there's a few people that are absolutely just killing it by embracing the technologies and using them. There's the chasm, you know, the Geoffrey Moore little chasm that we're crossing right now is that more and more people are realizing, like, oh, okay, I can use it for these things, you know, writing an automated script or, you know, and a lot of things that great technologists have used in the past, but now they can just do it in seconds instead of minutes or half a day or whatever, because they understand it at a level that the average engineer does not.

(Aaron Bare at 00:24:17) And I always think we used to—was part of this job board that we had this brilliant technologist that worked for the CTO. And we just said, if he's not wearing the right belt buckle today, that site's gonna fall apart. You know, he was the one that knew how everything worked, and every organization's kinda like that. Now you kind of understand that with these different tools, you can kinda protect yourself from that employee that literally has everything. So it's figuring out the redundancies. How do you actually create the scalability but having, putting the people that are really embracing it? And sooner or later, just like everything, as the technology adopts, you know, everyone's gonna have to adopt these technologies, you know. Everyone's gonna have to use these things to even compete or you'll just be obsolete. Because if you program—like, for example, if you were just a programmer and, unfortunately, I know there's some still some people that program in COBOL or some of these ancient things, you know, that's very, very inefficient compared to the platforms we program on today, right? So just imagine now if everything gets automated, where we're gonna be in five years.

(Aaron Bare at 00:25:26) And I think, you know, the one thing is change is not slowing down. It is absolutely accelerating. And to Matt's point of self-preservation, that's gonna go out the window quickly where they'll have to adopt these things to even keep their job. And I think that's what you're really seeing when you see these mass layoffs and bloating and everything. It's like, okay, the people that are absolutely against new tools, their productivity or their performance isn't as great and, you know, you start to see it in output, which is, unfortunately, what we measure engineering on, is, like, what are we actually producing? What is the innovation? What is the ROI? You know, what's the business case that we're solving for? And I think more and more that'll happen because AI tools are gonna enable those engineers to think more like business people to say, oh, okay, well, I just created a widget that saved our company $42 million.

(Aaron Bare at 00:26:13) Well, when you do that, you have job security. But the reason you did it is because you're literally leveraging this technology that everyone else has access to. But you've played with it enough that you understand what it is good at and what it's not good at. Like, it's not good at everything, right? It's not the singularity yet, but we're on a path that we're somewhere between here and singularity. We're somewhere between it, but we don't know how far along we are. But some engineers probably, more than us, know how far, you know, they are along, you know, further along.

(Brian Singer at 00:26:47) Both of you touched on a couple of points. I think the fear of change is huge, right? It doesn't matter if you're in a business organization or you're in a technology organization. I'm consistently brought into companies to really change culture and delivery. That's change. And it's almost peanut butter spread and cookie cutter the way the reaction is. "Well, it's the constant, right? That's not the way it's done." "Well, why?" "Well, it's because that's the way we've always done it." Well, the way you've always done it is not always the right way to do things, right?

(Brian Singer at 00:27:25) And when you're working with engineers, typically, the engineers are more open to change because it's just another problem to solve. I've run into more business folks being oversensitive and cautious to leveraging these technologies. And I personally, I look at it as the biggest challenge obstacle for me is data integrity. I'm sure you all see or have witnessed solution hallucinations on ChatGPT, right? I've looked myself up and it couldn't find me, obviously, because, you know, the data was old. I looked myself up and, you know, I'm Brian Singer, the director of X-Men. And then I've looked myself up and, suddenly, I am the CTO of some company in London. And it's this hodgepodge of my career in this London company. And I don't know where it's coming from.

(Brian Singer at 00:28:15) So I think there's definitely reluctance when it comes to that. However, when there's true or false, like binary situations, it's very good at binary, like coding, right, for example. And anyone who's just gonna take a snippet of code off of ChatGPT and implement it and not do any validations, well, shame on you. But from a binary perspective where you don't allow it to deal with ambiguity, very, very, very solid. I got more of the reluctance when, for example, I informed someone that I had used ChatGPT to do something. That's when the fear came in. So I do think there's gonna be this learning curve and trust, not just between the engineer or anyone who's using the functions, but the consumer of what that engineer or user of ChatGPT produced. I do think that that trust has to close, and that's when you'll see those thirds start to shift in different directions. I border on all three of those, to be honest with you. I use it. I've used it to execute on things. I've used it to write a document, even though I like to write and I'm pretty good at it. I just really wanted to see what it would do, and it turned out really well. And I wordsmithed it into my own tones.

(Brian Singer at 00:29:36) I've also, you know, done some things that I just thought were cool. Like, my, good example. You know, my wife caught me watching South Park, and she never watches South Park. I mean, in 30 years, how do you never watch South Park? And she's like, I don't even understand what this is. So I went to ChatGPT, and I said, "Tell my wife—explain to my wife what South Park is all about." And then wrote this beautiful, like, you know, three-paragraph page summary. And I said, "Just read this, and you'll know everything about it." And it was fun, right? It was kooky, and it was fun. And then I've run into the distrust, like the example I gave about, you know, searching for myself. But all of that, it's the intelligence, right? It's the intelligence factor, and it goes back to the prior points that both Matt and Aaron were making, and that's, look, the smart people, they're gonna figure out how to use this, and they're gonna ignore the anomalies that they're getting when they're engaging with the generative AI components, Copilot, ChatGPT, you name it, right?

(Brian Singer at 00:30:29) And they're just gonna continuously try. It's just like, you know, anybody trying to Google a term, knowing full well that Google will produce that result so long as you keep iterating on the phrase and keywords that you're searching on. I just, you know, it's just about that kind of learning curve. And, frankly, maybe Google is a good example or analogy to it in terms of how people started to trust, you know, Google over Ask Jeeves, right?

(Joel Beasley at 00:30:58) It got me in trouble. So for Valentine's Day, my wife wrote this beautiful post on Facebook because that's what women do, right? She wrote this beautiful post and collaged all of our pictures and everything. And I went up to her. I said, "Wow." I go, "That is so well written." I go, "I never knew you had that inside of you," because she, historically, is not the best speaker, right? And I was like, "This is brilliant." I was like, "I can't believe this." And then she looked at me, and she goes, "Well, I use ChatGPT to write it." And I was like, "Oh, no." It can get you in trouble, so be careful if your wife writes you something beautiful that's unusual. It might be ChatGPT. But next conversation is, what's going to happen to human—and Matt can start. What's going to happen to humanity when generative AI gets to the point where I can't tell the difference between if I'm talking to Matt Peters or an AI version personality, right? What's gonna happen to humanity? Are we going to put more pressure on in-person work or conferences and getting together? Like, how are we going to have trust with each other and build these relationships when we can't trust what we're seeing on the screen?

(Matt Peters at 00:32:12) Alright. Thank you for assigning me to start a question that begins with what happens to humanity. So, well, unfortunately, I think there are probably several answers because there are several audiences for that question. So the way that I think about that, I do worry about it. And I think, you know, Brian and Aaron were both making good points about mistrust of tools like this.

(Matt Peters at 00:32:32) I think it will amplify that level of mistrust. That'll create a lot of problems for everyone, but I'm more concerned about, as we push toward—those of us who were used to being able to trust one another in work and social settings, we'll probably want to pull back and return to a little bit more in-person. I want more of that. I want more of that connection. But as I look at more of the generation that's coming into the workforce right now, in order to be able to secure them, we have to be able to offer them a fully remote job.

(Matt Peters at 00:33:04) And the introduction of more and more of these technologies, more and more personalized media experiences, everything that's happening around and preceding what is probably the forthcoming wave of unprecedented deepfakes and identity challenges, I don't know what to expect them to do. They're already looking for that very insulated and very individualized experience. So what they would reach out toward or what they would compare to, you know, the before times, I don't know how they're going to reconcile that. And they're actually—I mean, they're the audience that I worry about the most in this regard because I think they're, because of that disposition, if that's even a fair way to characterize it, and I don't mean to cast entire generations into one big bucket, but it strikes me that they will have a tolerance for that mistrust or distrust that we currently as old men don't have. And that really is where I believe the erosion of humanity overall would begin, if it does.

(Matt Peters at 00:34:07) If they decide they don't have to care anymore, then we've really taken some foundational building blocks away, and we don't have very much left for the structure to stand on anymore.

(Brian Singer at 00:34:19) We've talked about this a few times already in this discussion, and that's part of what is going to come out of the rifts in big tech and ChatGPT is innovation. So I look at this as an opportunity, and I'm going to copyright it as a part of this conversation. I'm just joking.

(Matt Peters at 00:34:43) But think—

(Brian Singer at 00:34:43) Think about this, right? When ticket brokers went online, people created bots to go get to the front of the line and buy tickets. Now there's, you know, protagonists that are building reverse engines to block or get you as a person as opposed to a broker inside into that seat. We can do the same thing.

(Brian Singer at 00:35:04) Right? So if you're concerned that it's a deepfake, if you're concerned that a thesis has been written by generative AI, then it's our ability to build software that can detect whether it was actually consumed and output by generative AI. Is it a deepfake? I go back to innovation. We've always been able to innovate to prove dysfunction or to prove that something is not as it was, whether that's spy games or trust games or lie detectors, et cetera, right?

(Brian Singer at 00:35:41) I just see the same thing happening here. It's not trivial, don't get me wrong, but you're already seeing some of that take place. Gosh, I can't remember where I was, but I was doing something online and there was a real detector. Right? It was, what's the percent chance that whatever your output is written on?

(Brian Singer at 00:36:01) I think it was actually Copilot, if I remind myself. Based off of how I was writing my document, whether or not it would be interpreted as being written through generative AI. That's where I think we go to be able to prevent that from taking over and, you know, causing a disruption in all of humanity.

(Matt Peters at 00:36:19) I hope you're right, but I think you're also painting a picture of—

(Brian Singer at 00:36:23) Oh, yeah.

(Matt Peters at 00:36:24) Of a threat actor scenario where we're putting ourselves in the position that the good guys have to chase the bad guys. Historically, that's not always been a winning position to be in, and I think because of the proliferation of generative AI and the prevalence—and not just the prevalence, but the fact that it's practically free now—the barrier to entry to throw a deepfake out there and to try to use it and weaponize it is really low compared to where it used to be. It used to be an expensive proposition to really try to put that forward and disrupt any kind of process or procedure. Now it's not. So I think that as that grows and that really starts to affect every avenue of every life, right, it won't just be elections.

(Matt Peters at 00:37:04) It's going to be prevalent in high schools and middle schools. It's going to be at jobs. It's going to be everywhere. It's going to turn into one of Aaron's wicked problems that needs real growth mindset to focus on and real exponential thinking. But, I mean, I want to optimistically believe that you're right, Brian, but I think it's a tough spot to be in as well.

(Brian Singer at 00:37:25) Spot on.

(Matt Peters at 00:37:26) I'm not accusing you of trivializing it, but it's big.

(Brian Singer at 00:37:30) No, I don't disagree with you. It's almost like a new generation of hackers, if you will, instead of, you know, more of a white hat approach to it than a black hat approach to it. I am more optimistic. Don't get me wrong, because you see leaders of big tech also understanding that this can pose a significant risk to people, including ChatGPT's creator. Right? You know, great. I'm glad they all signed the petition, but that's not going to really stop anything.

(Brian Singer at 00:37:57) It's not going to stop anything at all, right? It's out of the bag, and the threat actors are already using it, and they're figuring out ways to, you know, leverage it to, again, stretch the truth and develop false truths and use it for propaganda in terms of what they're favorable for. But it's definitely an opportunity, again, for additional technological advancement on the flip side. There always seems to be, and that's a leap, right? Reciprocity to advancements in technology, both the good advancements and the bad advancements.

(Brian Singer at 00:38:38) So, you know, it's scary. I just do have some hope that these tech leaders are going to start to invest some funding, some real funding, into being able to identify what is and what is not leveraging generative AI.

(Matt Peters at 00:38:54) I mean, along with that investment, I think we also need some kind of investment or a sea change in culture that creates the demand for it among all users, right?

(Brian Singer at 00:39:04) It's one thing—

(Matt Peters at 00:39:04) It's one thing for us to introduce a technical solution to a problem, but if people don't care for the problem to be solved—people don't care right now—you waste energy and money.

(Joel Beasley at 00:39:13) Yeah. If I went and, you know, Brian tried to sell your company a solution for $50 a year that's going to authenticate that the person on the other end is actually the other person, that's a hard justification for that spend, right, today?

(Brian Singer at 00:39:25) Absolutely.

(Joel Beasley at 00:39:26) Aaron, I want to hear your thoughts.

(Aaron Bare at 00:39:28) No, I think everybody said a lot. I mean, I think it's, you know, we're in this place that—and I was going to, heck, I was going to glitch a little bit and say, how do you know I'm not generative AI? But, you know, like, just coming back and forth. But I think, you know, we're at a place where the technology is only going to get better.

(Aaron Bare at 00:39:48) The barrier to entry, I think, as was shared, is very, very low. It's going to become nonexistent. And we're going to have to start not trusting the news. I think, you know, as Matt said, the masses are the ones that are going to be most impacted by this because people they trust, they're going to hear things and this already happens. You know, I can talk about the former governor of, um, Ricky Rosselló of Puerto Rico.

(Aaron Bare at 00:40:13) You know, all the media kind of ganged up, created all these text messages from all the different parties. We're able to look—make them—but when they went under and they found out that they didn't come from the IP addresses of his phone and all these different things, which took a year and a half later. He had been removed from office, you know, was considered, was canceled because he had bigotry and all these different things. You know, by the time they did an independent investigation, which was really supposed to hang him hard, they're like, "Uh, actually, he didn't say any of these things. Many of these other things were just made up."

(Aaron Bare at 00:40:46) And I think that's the early stages of how do we slow down the media, because media is so damaging. If I were to say there's one thing in the world that's impacting Hamas, Israel, Ukraine, Russia, the politics, you know, media is pushing a narrative to really get your amygdala to literally fire in a way that creates you to pay attention to it. And, unfortunately, that means that deepfakes, you know, as we've seen the jokes between, you know, Trump and Biden, you know, and they're sitting around the table telling jokes and you can kind of tell now, but in five years, you know, two years you won't be able to at all. And I think that's where the masses are going to have a hard time understanding that. I think that there will be technologies underlying that that create some authenticity.

(Aaron Bare at 00:41:40) I think, actually, blockchain has a potential, another rising to figure out how do you build trust between people. I think there's other technologies that are probably better than that. But, you know, all of a sudden we have to think about new solutions and I think it's to Brian's point—whenever there's a problem, we know that now 10,000 engineers, and that's the beauty of the innovative world today and not just in America, like all over the world. And, you know, they're creating problems and then there's people solving problems. And we're kind of moving at this innovation pace that'll be accelerated when you start stacking AI and 3D printing and drones.

(Aaron Bare at 00:42:14) And, you know, you imagine the problems that you start to kind of create as technologists. Well, those, as we go exponential and as we start thinking bigger, it's the 100x developers that are going to be able to solve that, you know. The really—the people that have put the most damaging kind of stuff out there are the 100x bad developer. Like, the one that puts a cybercrime, you know, that puts something, a Trojan horse that impacts everybody. It's not someone that just started coding.

(Aaron Bare at 00:42:42) It's, in general, it's generally someone that's really worked out or working for a state, a foreign state. So all those things exist now. I think now as we look at my picture and you say, "Okay, he's real. We believe he's real. He's talking." Um, in five years, you won't know that, you know. And I think that's where we've got to figure out ways that we build trust. I do think we'll have, you know, some different technologies that kind of come up to authenticate several different things because even right now, someone could call you with someone else's voice and get you to expel information.

(Aaron Bare at 00:43:17) You know? And that's happening in cyberattacks. That's happening. You know, when you use—you know, I've done a lot of stuff in cybersecurity and own part of a company. And when you realize all these attacks mostly are just social and, you know, it's literally a social attack that they figure a way out and then they manufacture the technology to kind of exploit that.

(Aaron Bare at 00:43:36) And, you know, if we didn't have people, a lot of the cyberattacks would not exist or we would see them faster. But people are the ones sharing their password or sending things, you know, through different technologies that aren't secure. So we have lots of problems right now that are the same as generative AI. As it starts to kind of escalate, I think we'll have solutions that kind of combat those. Will it go hand in hand?

(Aaron Bare at 00:43:59) It's kind of like Moore's Law. We keep finding new technologies. Soon as the one technology runs out, we find another one. So it's not to say we're going into an uncertain world. It is going to speed up and it has to speed up on both sides.

(Aaron Bare at 00:44:12) But I think that there's enough opportunity and that's the beauty of the world we live in today. If we have a little bit of patience—and that's going to be the problem of the media, they won't have the patience. They're going to be willing to put this out there. You know, they'll see a video of someone saying something and they'll eventually put it out there. They're going to have to retract more and more and more.

(Aaron Bare at 00:44:32) And that's going to be, you know, an interesting—you know, media is going to be the one thing that they've got to figure out how they authenticate messages before they put it out there because they'll be responsible for some massive damage in the future. And to this day, they haven't really been all that responsible for things they put out. But if you're literally putting fake messages out that ruin people's lives, you know, like what happened to the governor of Puerto Rico. Um, all of a sudden you kind of realize like, wow, you know, we live in a different world and we need better solutions because, you know, we are canceling people very quickly today, you know, based on what we see and what we hear. And honestly, that's all the masses have to do and then they will share it over and over again.

(Aaron Bare at 00:45:17) Right? Like, if something's juicy, you know, people are just like, "Let's send it out" and they don't think about where it came from. And I think we've got to create some solutions there. So I'll put that to all the listeners out there. Someone's—

(Aaron Bare at 00:45:29) Got to start working on that today to solve for it because it is going to be a problem in two years.

(Joel Beasley at 00:45:34) Well, I talked about it with the chief information security officer at Salesforce. He is most popularly known for creating the project of SSL, right, way back in the day. And that problem emerged because of all the online transactions that were happening, and they needed a way to protect it. So several different companies and their engineering teams got together, formed this group, and then ultimately, the output was the SSL certificates. I think that it will likely be a mix of us not trusting each other or not trusting what we see on the screen, just distrust for what we see on the screen and weighting it differently in our lives about the relationships of the people we actually know mixed with a technology that none of us see today that will come out and be some way to do some form of reputation or end-to-end confirmation that it is that creator likely with some sort of maybe blockchain data store or something, and then you trust the store historically.

(Joel Beasley at 00:46:35) Right? But I do think that the next generation is going to have a lot less trust for what they see online growing up and what they're seeing today.

(Brian Singer at 00:46:46) Absolutely. I think we already see that the younger generation doesn't trust anything. Oh, yeah. You know? That's why they're all on TikTok, and that's about it.

(Brian Singer at 00:46:55) The only thing that they get out of it is what they're seeing other people do and following that, and they're ignoring what would be traditional news media that, you know, Aaron was very spot on in terms of mentioning.

(Joel Beasley at 00:47:08) By the way, have you looked at those numbers? Have you looked at those traditional media numbers about, like, what views, like, Fox and CNN get compared to—

(Brian Singer at 00:47:17) Oh, yeah.

(Joel Beasley at 00:47:17) A streamer online? It's ridiculous.

(Brian Singer at 00:47:20) It's too amorphous. Yeah. It's not even close. It's very disturbing.

(Matt Peters at 00:47:25) Brian's right. They're going to TikTok as an alternative, but they're also trusting TikTok with no verification obligations or any code of ethics whatsoever, and they're just okay with it.

(Brian Singer at 00:47:37) I don't even know how to—

(Matt Peters at 00:47:38) I don't even know how to handle that. I mean, I've got two daughters. I want to hug them and choke them almost all the time. I just don't know what to do.

(Brian Singer at 00:47:47) It cracks me up because I remember, you know, when we couldn't use Wikipedia as a source, you know, because it's crowdsourced. Correct. Geez. Look at Wikipedia now.

(Brian Singer at 00:47:58) It looks like a pretty good Encyclopedia Britannica compared to all the other avenues of getting news, right? I sum it up similarly to what Aaron said. This is just a push for more innovation. Think about being an engineer coming out of school right now. You have all sorts of different avenues that you could choose from to specialize in, in terms of data engineering. You have information security, which likely is what I would do as a specialization if I had the chance to get to do it over again, because that's perpetual.

(Brian Singer at 00:48:35) We're always going to need that type of engineer. And now you've got generative AI and different threat actors, different cybersecurity threats, not necessarily looking to hack you or disturb you socially or financially, rather just to fake you into believing quote unquote news, right? Or swaying a political election or canceling someone based off of just having a bad day, and knowing that full well, you could go create a bunch of false messages. That's such an opportunity to be the generation that goes and leverages the same technologies that could be used against us to protect us from that disaster that could be waiting to happen. That's how I think we sum up this conversation. That's reciprocal. If you think about the flywheel from you get laid off, now what do you do?

(Brian Singer at 00:49:36) Figure out ways to use your skills to go prevent these threat actors from taking place, right, and leveraging the same technology. So I think there's a whole flywheel that you could look at through this entire discussion to see where the next generation of engineers should be taking themselves.

(Joel Beasley at 00:49:54) I want to make sure that we're good with everybody's time. So what I'm going to do is, Aaron, I know you were just talking. I mean, let's give everybody like thirty seconds to wrap up. Brian, if that was your wrap up, just give us a thumbs up. Okay.

(Joel Beasley at 00:50:05) All right. Go ahead, Aaron. Sorry for that.

(Aaron Baer at 00:50:08) So now I got thirty seconds. So I'm okay.

(Joel Beasley at 00:50:09) You got thirty seconds.

(Aaron Baer at 00:50:10) I got to be concise. No, I think we're, you know, no other exciting time in life to be this excited about the amount of change that's going to happen and the amount of change that you can create as an engineer in particular. And I think in the next window, like Brian said, I think information security is going to be more and more important as it has been every year that the Internet's been out, and it's become more and more valuable.

(Aaron Baer at 00:50:41) And I think part of that is when we look in the future, we see these narratives that have made our society kind of extremes, right? And we see this in politics where the middle is getting bigger, but you see people following these extreme narratives, often to the oblivion. So I think the algorithms, I think we're going to have algorithm authentication. I think it's in essence what Elon Musk is attempting to do with Twitter. It's like, how do I kind of use an algorithm to give you a better experience but not necessarily put you in a different place that you're just listening to one narrative?

(Aaron Baer at 00:51:19) You know, because the great thing about a newspaper that folded, which for all you young listeners out there, that was something that you got every day and you opened up and you got ink on your hands, but you got to explore different subjects that you may not explore. Right now, that's really hard. I think the important part is the future is we're going to have, with AI, the ability to kind of understand what are the dopamine hits that allow us to kind of be curious and explore different areas. That's all coming. That's an opportunity that doesn't really exist now in the platforms.

(Aaron Baer at 00:51:53) They're very much like, hey, you like this content? I'm going to give you more of this. And then it just starts to veer one way or the other into extremes. So I think there's a real opportunity obviously to create authentication, to create a custom experience. I think about, you know, it's kind of like health care. Health care is a one size fits all. Here's a pill. We're going into this bio revolution that's going to be like personalized medicine based on your chemistry, not necessarily your genes, your epigenetics, your exposome. I mean, all these different things that we see that are transformative.

(Aaron Baer at 00:52:30) The Internet and your attention to media is going to be the same. Meaning, everyone should be on a media diet, meaning they have to understand what they're consuming. Is it good for them or does it serve them well and inform them? And I think we're starting to find tools that are starting to do that. And we're just going to be in an incredible innovation explosion that probably will feel like chaos to the masses but will ultimately get us back full circle, to where technology will stop using us and we'll get to use technology.

(Joel Beasley at 00:53:05) Matt?

(Matt Peters at 00:53:06) Yeah. I think one of the things that strikes me as interesting, a lot of the conversation that we've had today is really about ways that technology is changing the landscape of work and what people want to do and can do. The thing that we haven't talked about being taken away, and I don't think it will be, is the ownership of that work, the liability, accountability, and responsibility for the output. This is still a tool. We talk about it as an alternative to a human workforce, and it simply is not.

(Matt Peters at 00:53:32) The thing that I think is exciting right now and that I want to believe is currently driving the high peaks and low valleys of tech employment and the volatility around there is that with generative AI being pervasive absolutely everywhere, more so than any technology I've had the pleasure of enjoying in my lifetime so far, it has also, to borrow a few terms from Gartner, it has gone screaming up toward the peak of inflated expectations faster than anything else I've ever seen before. Logically speaking, I feel like we've got to be getting close to the top, which means that we're going to plummet into the trough of disillusionment faster than we've experienced with a technology platform before. But then I also want to believe that will also mean we will make our way to that plateau of productivity quicker as well, and that will allow us to experience a little bit more normalization of the employment turmoil that we're experiencing right now. And I hope, to both Brian and Aaron's points, that a good bit of what will shake out of that will be new innovations, new startups, people who go off and create a new gig and find a new compelling sense of purpose.

(Matt Peters at 00:54:41) Now what I don't know and what I'm excited to see, and we just don't have data on it yet, with the inclusion of these kinds of technical capabilities for an individual who moves off and just starts doing their own gig, will it fundamentally move the needle on success rates of startups? They have historically been very poor, but now this changes the landscape of both the revenue requirements and the directions that you can take output in productivity. So if we can move that, and, you know, at the moment, I'll go a little bit U.S. centric right now. We enjoy a premium position with respect to AI adoption and understanding, but we are not miles ahead of any other country in the world. We are measured in much shorter distance than that in terms of our lead.

(Matt Peters at 00:55:32) But is that enough time for the U.S. to really retake the reins and really become a major innovation center and a major startup hub, like we have been at a lot of points in the past? But I'm not confident that we are right now, but I think we could be. So hopeful. I'm a cautious optimist with respect to this whole conversation today.

(Joel Beasley at 00:55:54) Awesome. Guys, we did it. We had our first ever panel Modern CTO discussion. Thank you so much. Thank you so much for listening. And if you found this episode useful, please share it with a friend or colleague who you think would get value from it.

(Joel Beasley at 00:56:09) And if you have topics that you would like to hear discussed on the podcast, either add me on LinkedIn or send me an email, [email protected]. Every time I get an email or LinkedIn message, it absolutely makes my day and inspires me to keep going.