Change by Design: Engineering Transformation with Chad Jackson
Change by Design: Engineering Transformation with Chad Jackson
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The Human Problem Engineers Don’t Train For: Driving Change at Boeing | Dr. Ryder Dale Walton

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OM DENNE EPISODE

What does a youth minister turned Boeing AI engineer have to teach engineering executives and change agents? As it turns out, quite a lot.

Dr. Ryder Dale Walton has spent the last decade driving two of the most consequential transformations in aerospace engineering: the shift from waterfall to Agile, and the integration of AI and large language models into engineering workflows. In this conversation with Chad Jackson, he offers a perspective on change leadership that you won't hear from most technical practitioners — one that's as grounded in human psychology as it is in engineering discipline.

Ryder makes a case that the soft skills change agents most need are the ones they're least likely to have been trained on. When subject matter experts resist change, data and dashboards won't move them. What moves them is understanding what they stand to gain — and being patient enough to show them. His background in ministry and the arts, he argues, gave him the "ambidextrous brain" that makes the difference between a change initiative that gets funded and one that actually gets adopted.

The conversation also gets into the real-world mechanics of change execution — why deploying fast beats deploying perfectly, how Agile adoption actually works through mentoring rather than training, and how to navigate the hardware-software integration challenge as product complexity explodes across aerospace, automotive, and defense. And on the topic of AI, Ryder offers a quietly unsettling observation: when machines automate the creative work, they may be taking away the very activities that help us recharge as human beings.

Perhaps most relevant to change leaders: Ryder sounds a clear warning about burnout. When people are already stretched thin, the emotional bandwidth required to genuinely engage with and bring people along through change simply isn't there — and that's when initiatives quietly die.

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Dr.
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Ryder Dale Walton.
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Uh, thanks for coming on the show.
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Thank you for having me.
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I appreciate it, sir.
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Yeah, looking forward to the conversation.
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So, uh, a way that we often like to start off these conversations is to understand a little bit of your background.
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Uh, Right.
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your career journey been? What are you doing today? Uh, and like how did you walk that path to get there? Uh, that's a great question.
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So, um, I'm currently, um, AI and a machine learning technical lead engineer for the Boeing company.
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Um, sort coded as an engineering data scientist.
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But that's definitely not how I started.
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Uh, my, uh, bachelor's degree is in Bible and ministry.
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And so I was in, uh, youth ministry for a couple years.
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Um, but I wasn't making very much money.
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It was the 250, 300 $50 a week, and my wife and I were married at 19 and 20.
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And so we, I realized pretty quickly that I probably needed to do something else.
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Um, and so I pivoted.
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Uh, but my minor was in, uh, biblical languages.
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And so that's something that I think that, that's maybe an interesting point to the story is I thought, well, you know, I'm just like obsessed with Greek and Hebrew and, uh, did undergraduate research in Greek.
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And I thought, well, I can, maybe I can pick with that language thing and, and take that thread and follow that through.
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And so I said, I'll just do languages with the computer, you know, uh, and so that's how I got into computer science.
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So I started doing that.
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Um, well truly I started in computer engineering 'cause there was a scholarship opportunity there.
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Um, and so I did that and then my academic advisor said, Hey, you know, um, I know you've already graduated.
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I know your wife's already working.
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Um, 'cause I had kind of gone back to, to get this extra degree.
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And he said, if we kind of recast your approach and, and call you a software engineer, we could probably get you the master's program here with a few, with some leveling courses, and then you can probably graduate at the same time as with this, uh, computer engineering degree.
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And I said, well, Yeah.
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Um, and so I did that and Boeing hired me at like a career fair.
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Uh.
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You know, my academic advisor helped mark all that up and like the very next career fair like that was on campus at the Oklahoma Christian University, Boeing was there, and I just got this email out of the blue.
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I was like, well, are you applying or not? And I'm still not a hundred percent sure if they emailed, if they, if they had intended to email that to me or not.
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But, uh, I saw that as, you know, divine intervention and so I jumped in and uh, got the job.
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Then I worked in as software lead for, oh, about three years, uh, software, uh, human computer interface software, and so gooey designs and things like that.
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And then, uh, I was working on a specific program that later gave me the opportunity to go do a field service, like engineering liaison work.
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And so I moved to Japan.
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To do that.
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And I was there for about two and a half years, just a little bit longer than that.
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And then came back, uh, when my family and I were done there and landed in this, uh, digital transformation space.
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So that's kind of how we got here.
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So it's been, it's been a ride the last decade.
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Uh, it is pretty much, that time span's been 10 years.
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Yeah.
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What a story.
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Holy cow.
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Like we never know where life is gonna take us.
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Right? I Right.
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Yeah.
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that's, true everybody.
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It's all providence and fortune.
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You know, I didn't plan, I really didn't plan that.
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There wasn't any part of that that was really on purpose, you know? Um, it was just the doors opening, and then I took the doors and, and here we are.
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Yeah.
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Yeah.
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That's great.
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Oh, wow.
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Really crazy.
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Yeah.
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let's let, let's shift gears.
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Let's talk about change agents.
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Okay.
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know, you, uh, you've touched on digital transformation.
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Yeah.
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talked on, you know, you've touched on ai.
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Mm-hmm.
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let's start off, like to ask this question.
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How do you define a change agent? What's your definition? that's a good question.
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So, I mean.
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As kind of a lingual file.
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I just sort of break that down into the two terms that we have, you know, agent, somebody who has agency, uh, someone who you can affect things you have the ability or the permission to, to make affected change.
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And so, um, that's kind of how I'd define it, you know, um, it, the way it's lived out or especially in, in larger organizations, maybe outside of like the startup culture, you know, there's a lot of, um.
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Bureaucratic, uh, organism is what I'll call that.
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That's kind of at play when you're trying to, to do your work and you have to sort of be knighted, so to speak, to, to get that change agent authority, you know? Um, and that's sort of where I'm at.
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You gotta have the permission.
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Yeah.
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That's really interesting that you, uh, you use that term knighted.
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I've also heard, uh, people describe it as that they were voluntold, uh, to yeah, uh, that that responsibility, yeah.
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the way that people get into these change agent roles is incredibly vari.
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Mm-hmm.
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so how did it work for you? Did you have an executive come to you and say, I want you to do this, or did you volunteer or like, that's a great question.
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Well, so I was in Japan.
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And my, uh, wife and I were blessed and, and expecting our third child, and it was gonna be a surprise, you know, surprise baby.
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And so we were like, okay, what are we gonna do? And so this is the COVID era.
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I, I should make that really clear.
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Like, we moved into Japan in 2021 on like a triple seven that? only had seven passengers on it because of there was only essential travel, you know? Um.
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Wow.
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And so that was wild.
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And so we went there with a, like a 2-year-old and a, and a three-year-old.
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Um, if, if that even honestly, they were really little.
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And, uh, so we talked about it and there were still some restrictions on, uh, in the Japanese hospital.
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I wouldn't have been able to be there.
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Um.
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In Japan, they, they actually pamper new moms pretty well.
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They, they'll keep you at the hospital for like five days and, and take care of you instead of here in the US where it's like, Okay.
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you're done.
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They kick you out, you know, you Yeah.
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hours or so.
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So she's like, I don't really wanna be in there five days by myself.
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I don't speak Japanese.
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And I was like, Yeah.
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let's, let's get you home.
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Uh, and so, uh, I started looking for roles at that.
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point.
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And so I, I was the, uh, lead, lead, um.
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A lead field service representative at this time.
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So I had a team of, uh, great guys.
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Um, most of 'em are still there.
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Um, but anyway, I was just trying to find something and I was hoping I could get a remote gig.
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Uh, you know, while we were in Japan, my, my mother-in-law passed away from the Delta wave, uh, which was Yeah.
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Which I, I appreciate that.
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That's obviously damaging.
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We haven't quite gotten, um.
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I mean, you never do really get over that.
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I mean, it's one of the things that you, you cope with better over time, but it's, Yeah.
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it's a struggle.
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Um, so I was trying to find a remote job so we could live close to my parents and have my kids, uh, close to them.
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And so the company said that was okay.
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And, and the remote job that they had was, uh, digital transformation gig for, um.
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Basically a release train engineer that was gonna be a systems engineering position.
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And so we were, they were trying to kind of move into a larger agile, um, uh, setting across different teams.
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And I said, well, I've got some experience in my, my master's capstone, we had to run an agile project.
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And uh, you know, we had to, we were using version one at the time, you know, heaven forbid.
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But we were using version one at the time and, uh.
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Had to run the whole project, you know, do the front end, back end, API layers, build the whole thing and do the design documentation.
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And then our, uh, our, uh, master's advisor basically was like in our scrum, so to speak, and, and watched and made sure that we did every single step of that and who was doing which work items.
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And that was kind of how he graded it.
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And so I said, well, you know, I've done that with a team.
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I think I could probably stretch and do a whole release train, and, uh, took that job.
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And so we came back and moved back to.
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Northwestern Oklahoma.
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And, uh, Mm-hmm.
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that was, that was kind of how that happened.
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Wow.
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Yeah.
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Pretty So, yeah, so I just, I guess I just applied for the role.
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I didn't get voluntold or anointed, so to speak, but, Um, there, there was one available, you know? Okay.
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All right.
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Gotcha.
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Makes sense.
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Um.
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That's so interesting.
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Um, I know a lot of people would love roles like that.
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Mm-hmm.
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and, you know, you, uh, you, you hit it at the right time.
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Maybe Yeah.
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maybe unknowingly, right.
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Right, right.
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great.
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And I'm back in office now.
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Uh, the, the culture's kind of changed.
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It's been long enough.
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So we did RTOs, um, alongside the, well, maybe slightly, slightly delayed from when the, uh, the government workers went back.
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Gotcha.
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Yeah.
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ton of sense.
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So you've been involved with some change initiatives, it sounds like The Agile was an example of that.
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You, you mentioned that you're working ai, uh, now.
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Yeah, could you, could you maybe give a high level.
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Overview of the types of change initiatives that you have worked on as much as you can.
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I know Yeah.
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Yeah.
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constraints, but for Sure.
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Certain things I can't say, but, um, the, you know, I think the two main, uh, changes I've been involved in was, uh, in like the software engineering community, uh, early on.
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Adopting agile when we had been doing more of a, a gated waterfall flow.
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Um, Mm-hmm.
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and so that was early career primarily, and then coming back as a part of a release train, more of the scaled agile framework approach.
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So that was pretty new to me.
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Uh, but also to the, the organization and, um.
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At least I say, I say pretty new.
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I mean, they'd been doing it for a while, but there was, um, going about it a slightly different way with different people, you know.
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Um, and then the second is the Agile or the, uh, ai, um, transformer explosion, if you will.
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Right? So Yeah.
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the chat GPT and that, um.
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Attention is all you need paper from 2017.
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And that kind of six years-ish later is when we start having GPT that people are like, wait a minute, this is kind of cool.
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Um, and so, um, that is probably the second major thing.
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So how we plan and do our work.
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And then there's this massive, um.
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Transformer power to AI capability.
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That largely is, um, an integration opportunity, uh, is how I tend to think about it.
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Um, and so those would be the two that come to mind.
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Let's, let's talk a little bit about, um, success factors.
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Okay.
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There's types of things that can influence whether there's a bunch of friction and resistance in initiative, whether you achieve the outcomes that you wanted to achieve.
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I'd love to get your perspective on what are the contributing factors, both positive and and negative.
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Um, okay.
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So I think there's always human psychology.
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Um.
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At play, you know, when we, when we kind of try and figure it out, um, you know, success in terms of like adoption, um, we can do a whole lot of things in the digital space to try and make processes better, um, to make design better, to, you know, digital threading or twinning or integration of different systems.
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But if we can't get the, the deployment, um.
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Of that tool, that a proper level of like human readiness so that people can, can see the value in it and then be willing to adopt it and make that change.
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We have to make that, that barrier as low as possible.
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Then basically everything that we spent was a loss.
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You know, there's no real return on investment if we can't get the buy-in, and it's very, very difficult to get people who are really knowledgeable.
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And experts about and, and systems that they have been using their entire career.
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And they know way more about you about this than you do.
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You know, you so they're the subject matter expert.
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You just happen to be this punk kid, you know, rolling up in this hot rod of software that, you know, with, uh, AI enabled software at that.
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Yeah.
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exactly.
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And so you're just trying to kind of, uh, you know, speed up.
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What they're doing in such a way that they will actually do it.
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You know, uh, instead of you, you burn, you burn the candle at both ends, so to speak, where you built this nice new thing that they just put on the shelf.
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'cause they, they don't want to use it.
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Um, you know, and so that's, that's one, uh, thing for sure in terms of like the human buy-in.
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Um, the other thing that I think of in terms of.
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When we use AI or we're going about trying to develop software engineering integrations, is I, I'm, I'm really big on deploying, um, more so than scaling.
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And not to say that scaling's bad 'cause that, that, that would get me beat up, but, you know, scalability is, is an important thing for sure.
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But, uh, you know, I think a lot of AI projects, um, and AI enabled and integrated projects fail because the emphasis is thinking really big.
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From the beginning, I wanna scale, scale, scale, this solution where if you can't fail, um, or at least your likelihood of failure is much, much less.
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If you think deploy, deploy, deploy.
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I need to get the MVP minimum viable product ready in such a way that I can get that to somebody and then get it to them.
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And then using agile, iterative, re-releasing, you know, we can scale it as needed or redesign or refactor while we're trying to get ROI.
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You know, just as quickly as we possibly can to, to make sure that this isn't a waste of time.
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really, really interesting.
217
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I mean, great, great perspective there.
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Thank you.
219
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Something that, something you said that caught my attention is that kind of first human element, uh, human psychology component.
220
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Um, and this is something that we've seen in a lot of podcasts.
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This is something that we've seen in research that we've conducted, survey based research that Oh, okay.
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Where we ask people, okay, what are the biggest challenge to this initiative, whether it's MBSE, Mm-hmm.
223
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definition, digital engineering overall, and what we're finding is people resistant.
224
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People resistance.
225
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People Mm-hmm.
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Mm-hmm.
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the top thing all the time.
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And that naturally makes me think about, okay, for change agents.
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Mm.
230
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Especially in engineering, the emphasis has frequently been on technical skills, Mm-hmm.
231
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but what we're seeing in the research is that the biggest challenge is a people problem, Yeah.
232
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our change agents kind of trained and armed and enabled with everything they need to kind of meet and work with this people's resistance type of thing.
233
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I'd love to get your perspective on.
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Our change agents today, especially for engineering stuff, are they getting the right Mm.
235
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Right.
236
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Are like whether it's training or insights or anything else, like what's your perspective there? I think that's.
237
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a really good question.
238
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Um, I, my personal experience with this has been that it has been a great boon that I.
239
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Did my initial research in, in ministry and in the arts.
240
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And, um, I spent a lot of time, you know, like doing, doing undergraduate research.
241
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So I had done a lot of writing before I ever tried to pursue my doctorate.
242
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Um, and I found that that kind of gave me a approach, almost an ambidextrous brain approach to, to learning in a way, right? So I'm doing, it's like right brain, left brain stuff.
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Which I think is really conducive to soft skills and hard skills.
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So I, I, I think when I was kind of hearing your question, I was hearing the kind of thought of like, what can we do to, to better communicate and, and better people do the people in better.
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And, and I think that's just absolutely mission critical and I think that's what a lot of like, um, folks that are, you might see as like a visionary might do particularly well is they might cast that vision.
246
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But because they have the skills to try and, uh, to get that buy-in and get other people to see their vision and then work toward it, that they're more successful.
247
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Whereas if I can sit down and, you know, we start doing hand operations on, on matrices, like unitary matrices and calculating a, a quantum circuit from, you know, gate to gate to gate all the way through by hand, that doesn't get anybody to actually use a quantum computer.
248
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Uh, you know, and so that's, uh, that's kind of how I think about it.
249
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And, and you mentioned training.
250
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Um, I don't really think, um, in my experience in industry, um, you know, not just with my current employer, but just in general, has been that it's difficult to get training that people can fit into their overwhelmingly busy lives.
251
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You know? 'cause a lot of times you'll get training or you'll have like a Toastmasters opportunity or something to help you with.
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With communications or, or presentations or soft skills.
253
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But a lot of that stuff isn't necessarily, um, you, it, it's additional training.
254
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It's not your day job.
255
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So whether you're trying to get another degree or it's just kind of off the clock, technical or a soft skills training, those sorts of things, it's just hard for, for people to fit that in, you know, when they're having families and commuting and.
256
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We're busy as busier now than maybe ever, and more unhappy now than ever.
257
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'cause we're on our screens, you know, 11 hours a day, you know? And, um, I think all that sort of factors in at varying degrees because, uh, you'll have people who are just, and I've struggled with this from time to time, you're just overwhelmingly like overwhelmed.
258
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And so you just kinda have to figure out like, do I really wanna be.
259
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Um, you know, on my screen eight or nine hours a day, and then I need to do, you know, two or three hours at night to try and develop another skill.
260
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And then maybe I'm overworking myself and that's making me more depressed.
261
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And then it's harder for me to, to interact with people in a real meaningful way in the office.
262
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And so there's all of that, I think plays in, you know, uh, to that people element.
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Yeah.
264
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Yeah.
265
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That's, that's a, I mean, I, I love, I love how you kind of summed that up it's such a great perspective because it's, I mean, it's absolutely true.
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Like if you don't, if you have no battery left to interact with the people Mm-hmm.
267
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you're leading a change, then it's gonna be really hard because people Yeah.
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part.
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Absolutely.
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And, and for a lot of people who get in these jobs, they're, you know, more introverted to begin with or they might be more like melancholic personalities anyway.
271
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And so it could be very, very draining to, to manage the people portion for sure.
272
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Yeah.
273
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Super interesting.
274
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Um, let's, let's, uh, shift gears Okay.
275
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bit.
276
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I'd love to hear from you, what do you find most fulfilling in this kind of change agent role, and what do you find? Not as fulfilling.
277
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Yeah.
278
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Yeah.
279
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that's good.
280
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I think, um, so the most, the most fulfilling part is kind of, it's almost getting to do the meta work in a way.
281
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Um, I, it's, to me it's very fulfilling that, um, you're not necessarily working on a specific contract for a specific customer.
282
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So you're kind of working above the work trying to figure out a way, how do we make this work better? And, and so that when we do this work for contracts in the future, it'll be smarter.
283
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And that's, that's a lot of what digital engineering is supposed to be.
284
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So a lot of the work, I would just call that, you know, meta work, we're, we're working so that the work that gets done by people who are better at their engineering jobs than me in the future will actually be more successful faster.
285
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With less quality escapes and, and, uh, less, you know, downstream danger to people who use products, especially in, on defense services and, and commercial aircraft.
286
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Um, so that's definitely the most gratifying part.
287
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It's, it's kind of like, um, you know, that architectural runway investment, you know, we do this work now.
288
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We may not see a lot of the fruits.
289
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Which is why I get that emphasis on, on deploy in my perspective, is we, we need to deploy, deploy, deploy.
290
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So we start to see fruits because we need that dopamine hit so we can keep running on, on this hard job of, of kind of building a new runway for other people to work and then that'll deliver for our customers.
291
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The ungratifying part is, um, I have this saying that I use a lot, which is bureaucracy demands, its sacrifice.
292
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You know, and, and, and the, the people that are doing that meta work or trying to lay a new runway also do all the paperwork.
293
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Yeah.
294
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So there's, the compliance element is always there and it's necessary, you know, so I'm not dogging it.
295
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It's just one of those things that, uh, that, that becomes you, your tribute for that.
296
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Yeah.
297
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Yeah.
298
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No, that makes sense.
299
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Yeah.
300
00:24:42,76.1738618 --> 00:24:43,246.1738618
And we're all the same, right.
301
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You know, things that some people enjoy about a role is dramatically different from someone else.
302
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So Right.
303
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That's true.
304
00:24:50,86.1738618 --> 00:24:52,306.1738618
ans all answers are legitimate, Yeah.
305
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Yeah.
306
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Absolutely.
307
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Absolutely.
308
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Yeah.
309
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I mean, Yeah.
310
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definitely helps to get the right person on the right job too.
311
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That's, that's a great point.
312
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Yes.
313
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Yes, absolutely.
314
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You know, it's so funny when we did this research, we have questions in there about like, how did you get into this role and.
315
00:25:11,146.1738618 --> 00:25:12,376.1738618
So many answers.
316
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And actually I'm writing an article about this that'll go into LinkedIn.
317
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So many answers were happenstance.
318
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It's like, oh yeah, well I just kind of showed an interest here and it was successful and suddenly I was tabbed and that's what I do every day, all day now.
319
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Um, so yeah, I think kind of matching, I think, I think an area and improvement.
320
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Both in for executives and for the people that might take the role would be, okay, what should the profile of this person be? And if you're looking at this role, like go into it with eyes wide open because it's not for everybody, Yeah.
321
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Yeah, I like that.
322
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Like the criminal minds profiling ki, you know? kind of thing.
323
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Right? It's like actually we profiling is a thing that we've done for quite some time.
324
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It might be worthwhile to put it a good use.
325
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Yeah, I hadn't thought about that before.
326
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I like that.
327
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absolutely.
328
00:26:10,246.1738618 --> 00:26:14,721.1738618
Um, so let's, uh, let's shift gears and talk Yeah.
329
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Okay.
330
00:26:18,21.1738618 --> 00:26:20,991.1738618
so I know that.
331
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If you look across the hard goods industries that, that we kind of cover, so automotive, aerospace, defense, Mm-hmm.
332
00:26:31,291.1738618 --> 00:26:37,406.1738618
um, heavy equipment, medical devices, the amount of software those end items.
333
00:26:38,386.1738618 --> 00:26:39,556.1738618
And the, I guess there's two things.
334
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The amount of software has been exploding, number one, right? It's just gone up and up and up.
335
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But I think I.
336
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thing that has been interesting, the footprint of software in those end items is no longer just on the product.
337
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Part of it sits off the product in the cloud or in some other kind of similar capacity.
338
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Um, I would just, I, I'd love to frame the rest of this conversation in.
339
00:27:06,496.1738618 --> 00:27:28,486.1738618
have you seen in terms of complexity, Hmm, in terms of growth of that complexity? just generically, what are you seeing and how's it affecting things? You know, and I can't, I can't help but think when we start talking about complexity, um, like smart thermostats and the internet of things.
340
00:27:28,576.1738618 --> 00:27:31,786.1738618
And, and that's kind of where I go at, at the beginning.
341
00:27:32,146.1738618 --> 00:27:35,56.1738618
Uh, 'cause I didn't realize that my toaster needed wifi.
342
00:27:35,446.1738618 --> 00:27:37,246.1738618
You know, the, those sorts of things.
343
00:27:37,246.1738618 --> 00:27:43,6.1738618
Or even, you know, in our house we don't have like a smart toaster, but we have a smart litter box for my cat.
344
00:27:43,426.1738618 --> 00:27:44,896.1738618
Uh, or we have two cats.
345
00:27:44,896.1738618 --> 00:27:48,46.1738618
Both, both Siamese cats, but they, um.
346
00:27:48,511.1738618 --> 00:27:56,131.1738618
You know, the, the, their cat box can communicate with my phone and tell me what I need to change the tray and I need to do all these different things.
347
00:27:56,131.1738618 --> 00:27:59,791.1738618
'cause it's like a robot rotates the, the litter around and everything.
348
00:27:59,791.1738618 --> 00:28:05,506.1738618
So I don't really have to go change it other than pull out the tray, dump the bag, put in a new bag, put the tray back in.
349
00:28:06,876.1738618 --> 00:28:09,61.1738618
Um, and there's advantages to it.
350
00:28:09,61.1738618 --> 00:28:10,831.1738618
So I'm, I'm knocking it because it's.
351
00:28:11,496.1738618 --> 00:28:13,501.1738618
We, we wanna talk about increased complexity.
352
00:28:13,501.1738618 --> 00:28:18,541.1738618
I now have to worry about whether or not my cat's litter box has wifi connectivity right now.
353
00:28:18,601.1738618 --> 00:28:20,371.1738618
'cause if it doesn't, it's not gonna operate.
354
00:28:22,441.1738618 --> 00:28:40,141.1738618
So, um, I think that's the best illustration I can think of when we go to just everything that, that goes on a jet or, um, you know, even like our inability to, um, sit in quiet and sit in boldness and just sort of let the mind.
355
00:28:40,831.1738618 --> 00:28:41,611.1738618
Wander.
356
00:28:41,761.1738618 --> 00:28:46,831.1738618
Um, we have to have like every single, uh, seat on a jet.
357
00:28:46,831.1738618 --> 00:28:53,251.1738618
It's gotta have someplace for USB, it's gotta have additional mounts where somebody can put their iPad or something like that.
358
00:28:53,251.1738618 --> 00:28:56,911.1738618
Or there's just a TV that's built into the back of all these different chairs.
359
00:28:57,781.1738618 --> 00:29:09,271.1738618
And so we wanna talk about, in a massive increase of complexity, is not just building something so that it has the right size and the right fit for, you know, this constrain constrained space.
360
00:29:09,331.1738618 --> 00:29:09,691.1738618
Um.
361
00:29:10,216.1738618 --> 00:29:12,976.1738618
On this aircraft, but now it is also a tv.
362
00:29:15,76.1738618 --> 00:29:25,426.1738618
And so I think that's, those are the sorts of things I always think of, you know, um, I can't get into to too many specifics on, on things that I'm actually, uh, working on or touching.
363
00:29:25,426.1738618 --> 00:29:28,846.1738618
But I, I always just use that as the examples for whatever reason.
364
00:29:28,936.1738618 --> 00:29:33,436.1738618
Uh, and I have some suggestions of what that reason might be.
365
00:29:33,526.1738618 --> 00:29:34,876.1738618
You know, monetization.
366
00:29:35,146.1738618 --> 00:29:39,16.1738618
And the way that our economy works is I need to build some product that.
367
00:29:39,571.1738618 --> 00:29:45,841.1738618
I can integrate in with your phone, which is the, that one device that steals your attention constantly.
368
00:29:45,841.1738618 --> 00:29:55,81.1738618
Even though the, the little g um, the little g gods of Silicon Valley, like Steve Jobs might not have, uh, allowed their kids to have iPads.
369
00:29:55,351.1738618 --> 00:29:56,701.1738618
We have done that.
370
00:29:57,541.1738618 --> 00:30:03,661.1738618
Um, and so most folks now have this, this device that follows the.
371
00:30:04,321.1738618 --> 00:30:20,761.1738618
Um, psychology of more or less addiction that gets you to use it all the time? Well, if I just connect my product to that, now I have an app, it goes in the store, I can get ad revenue and that makes, you know, lots of people, uh, maybe not lots.
372
00:30:20,761.1738618 --> 00:30:23,251.1738618
That makes actually a small collection of people, a lot of money.
373
00:30:24,331.1738618 --> 00:30:29,71.1738618
And so you can, you sort of see I think complexity being driven by market factors like that.
374
00:30:30,496.1738618 --> 00:30:35,926.1738618
And then we have kind of a, um, comfort creep that, that occurs.
375
00:30:35,926.1738618 --> 00:30:54,166.1738618
It's been called like Keeping Up with the Joneses in the past, or I'm, I'm currently reading through Michael Easter's Comfort Crisis, um, book, which talks about this idea of kind of comfort creep where, uh, I now need this wifi connectivity everywhere I go in order to be comfortable.
376
00:30:54,406.1738618 --> 00:30:55,486.1738618
And since we are.
377
00:30:56,761.1738618 --> 00:31:04,321.1738618
Fundamentally, human beings are desiring animals that, that seek comfort, desire, their creature comforts more than anything else.
378
00:31:04,321.1738618 --> 00:31:14,101.1738618
You know, I'm just as guilty and, and I think that that creep has caused an increase in complexity in everything because we can't just let a chair be a chair, it's gotta be a chair plus.
379
00:31:15,496.1738618 --> 00:31:16,216.1738618
Yeah.
380
00:31:16,216.1738618 --> 00:31:16,666.1738618
Huh.
381
00:31:18,16.1738618 --> 00:31:19,66.1738618
That's fascinating.
382
00:31:19,456.1738618 --> 00:31:22,36.1738618
That's, that's a fascinating driver.
383
00:31:22,36.1738618 --> 00:31:24,856.1738618
I had not thought about that before, but I think you're right.
384
00:31:25,96.1738618 --> 00:31:25,486.1738618
Thank you.
385
00:31:26,26.1738618 --> 00:31:28,696.1738618
it certainly is driving a lot of change.
386
00:31:28,696.1738618 --> 00:31:29,986.1738618
That's, that's fascinating.
387
00:31:30,46.1738618 --> 00:31:33,376.1738618
yeah, I'll take a look at that book that seems yeah.
388
00:31:33,376.1738618 --> 00:31:34,6.1738618
It's pretty good.
389
00:31:34,6.1738618 --> 00:31:34,606.1738618
It's good.
390
00:31:34,606.1738618 --> 00:31:41,356.1738618
And, and you know, I, I'm fundamentally a very religious person, so I read a lot of, like more, um.
391
00:31:41,896.1738618 --> 00:31:43,786.1738618
Usually more spiritual books like Dr.
392
00:31:43,786.1738618 --> 00:31:48,316.1738618
Martin Shaw's, liturgies of the Wild that's, that's come out, um, recently is a very good book.
393
00:31:48,856.1738618 --> 00:31:57,346.1738618
But, uh, Michael Easter has a more scientific approach where he is, he's citing doctors or PhDs and different studies and things in his, in.
394
00:31:57,346.1738618 --> 00:32:01,96.1738618
his book, but they actually fundamentally, completely agree on the problem.
395
00:32:01,246.1738618 --> 00:32:05,656.1738618
So I would recommend both of those books, uh, for, for that topic.
396
00:32:05,746.1738618 --> 00:32:06,46.1738618
Yeah.
397
00:32:07,276.1738618 --> 00:32:07,846.1738618
Very cool.
398
00:32:07,846.1738618 --> 00:32:08,716.1738618
Thank Yeah, you.
399
00:32:08,716.1738618 --> 00:32:09,106.1738618
bet.
400
00:32:09,346.1738618 --> 00:32:09,886.1738618
Of course.
401
00:32:11,821.1738618 --> 00:32:13,171.1738618
They're not paying me, by the way.
402
00:32:13,171.1738618 --> 00:32:13,981.1738618
I don't, I don't know them.
403
00:32:15,646.1738618 --> 00:32:18,31.1738618
you're not getting any ad No, no, no.
404
00:32:18,451.1738618 --> 00:32:19,651.1738618
This is not an affiliate link.
405
00:32:22,66.1738618 --> 00:32:23,311.1738618
um, so.
406
00:32:25,636.1738618 --> 00:32:31,996.1738618
Talk to me a little bit about like synchronizing hardware and software.
407
00:32:32,116.1738618 --> 00:32:39,376.1738618
I, I've seen a lot of companies in engineering and design and development really struggling.
408
00:32:39,406.1738618 --> 00:32:49,46.1738618
How do you reconcile the development cycle of hardware with the development cycle of software? Because there are fundamental differences There are.
409
00:32:49,116.1738618 --> 00:32:49,406.1738618
Yeah.
410
00:32:50,896.1738618 --> 00:33:03,286.1738618
And the problem that we see a lot of companies struggle with is get to the end, you flash the software onto the hardware, and lo and behold it, you know, you have major, major issues, Mm-hmm.
411
00:33:03,366.1738618 --> 00:33:03,376.1738618
Mm-hmm.
412
00:33:03,616.1738618 --> 00:33:15,166.1738618
Um, so I see all, I see a lot of companies doing all sorts of things trying to mitigate that risk, that late development cycle risk.
413
00:33:15,256.1738618 --> 00:33:15,766.1738618
So.
414
00:33:16,801.1738618 --> 00:33:22,41.1738618
What are you seeing? What's working, what are people trying? Um, Hmm.
415
00:33:22,81.1738618 --> 00:33:23,341.1738618
love to get your perspective on that.
416
00:33:23,566.1738618 --> 00:33:23,776.1738618
Yeah.
417
00:33:23,776.1738618 --> 00:33:25,366.1738618
that's a great question for sure.
418
00:33:25,366.1738618 --> 00:33:30,706.1738618
And I know that there's different, there's different kind of schools of thought for sure on this.
419
00:33:30,736.1738618 --> 00:33:37,66.1738618
And I guess the first thing I would add is most of my hardware experience ha, has been more in the realm of.
420
00:33:38,461.1738618 --> 00:33:48,271.1738618
Construction of like computing racks on site more so than, uh, like the integration of like a real-time software onto a hardware component or something like that.
421
00:33:48,301.1738618 --> 00:33:49,376.1738618
Um, Mm-hmm.
422
00:33:49,621.1738618 --> 00:33:55,891.1738618
and then I've done a lot of work with like, some hardware maintenance, capacitor reforming and, and different things like that.
423
00:33:56,641.1738618 --> 00:34:06,421.1738618
Um, so that's, this is kind of coming from that perspective mixed with the current mission statement of just working on like digital transformation.
424
00:34:07,186.1738618 --> 00:34:14,86.1738618
So I, I tend to, um, favor, and this is not to say that it's the right way to do it.
425
00:34:14,86.1738618 --> 00:34:19,186.1738618
'cause I, I kind of have competing, um, priorities I guess on this, this thing.
426
00:34:19,606.1738618 --> 00:34:24,616.1738618
So digital transformation is as much of, of, as much of this process.
427
00:34:24,616.1738618 --> 00:34:33,406.1738618
So I can sort of digitize and do my initial designs linking every phase or every step to the next step, to the next step, to the next step all the way through.
428
00:34:34,81.1738618 --> 00:34:38,401.1738618
And get my traceability and, and requirements and everything nailed down there.
429
00:34:38,851.1738618 --> 00:34:50,251.1738618
And then, uh, you know, ideally I'm able to simulate early on at various different layers so I can say, okay, we're passing the different simulation, like unit level testing, we're gonna move on to integration.
430
00:34:51,121.1738618 --> 00:34:56,341.1738618
Keep a lot of that digital because one of the competing priorities is just the fact that procurement will kill us all.
431
00:34:57,301.1738618 --> 00:35:13,501.1738618
It's just like a really, you know, it's an extremely challenging problem, uh, with supply chain and, and things that haven't necessarily recovered, at least in my lived experience from before COVID to, to after COVID is supply chain struggles are still difficult.
432
00:35:14,371.1738618 --> 00:35:17,401.1738618
Um, and that's just, again, that's just from, from my neck of the woods.
433
00:35:18,571.1738618 --> 00:35:28,591.1738618
So I have that is try not to have to buy anything or need anything as much as I possibly can because I don't wanna have to go through, um, you know, the supply chain hits.
434
00:35:29,191.1738618 --> 00:35:39,1.1738618
One thing that you can do to try and counteract some of that is additive manufacturing when we're talking about, you know, 3D printing, when we can go through form and fit checks and different things like that.
435
00:35:39,31.1738618 --> 00:35:46,501.1738618
Uh, and then that's not necessarily, um, that will not necessarily help your software integration piece, but at least you can.
436
00:35:47,206.1738618 --> 00:35:51,676.1738618
Figure out that the initial pieces of the hardware that I would be ordering are gonna go ahead and fit.
437
00:35:52,216.1738618 --> 00:35:54,646.1738618
You know, we can at least get that part, that part done.
438
00:35:55,276.1738618 --> 00:35:57,856.1738618
Um, so that's one thing you, you can mitigate.
439
00:35:57,856.1738618 --> 00:36:00,346.1738618
And then, so, and really those are kind of two strategies I guess.
440
00:36:00,346.1738618 --> 00:36:08,536.1738618
You can mitigate, mitigate the form and fit with added manufacturing, and you can mitigate the software and integration process as much as possible.
441
00:36:08,536.1738618 --> 00:36:13,576.1738618
And the requirements, functional logical architecture pieces through your digital threading your digital.
442
00:36:14,176.1738618 --> 00:36:23,446.1738618
You know, PLM system and try and do as much of that as you possibly can, but inevitably you're gonna get to that integrate that's gonna happen.
443
00:36:23,446.1738618 --> 00:36:42,16.1738618
You know, these two streams are gonna kind of converge and the question is, are gonna break the dam or not, right? So you gotta, um, that's where that deploy fast philosophy kind of comes back into play is, is I really do want to get a prototype as fast as I possibly can.
444
00:36:42,286.1738618 --> 00:36:42,496.1738618
Um.
445
00:36:43,186.1738618 --> 00:36:53,686.1738618
You know, all, all the things that I've kind of mentioned, if you don't already have those things built up, um, it, it can be really, really helpful to it.
446
00:36:54,196.1738618 --> 00:37:06,856.1738618
Maybe even think of it this way as like, I'm about to run these processes for, for, no kidding, the, the actual products that we're gonna build, but just to make sure that we're not wasting a lot of time at these gates.
447
00:37:06,856.1738618 --> 00:37:10,216.1738618
You might just jump all the way to the end really fast.
448
00:37:10,891.1738618 --> 00:37:22,681.1738618
You know, flash, flash, a empty hello world project of the tech stack and everything that you're gonna use onto this other device and make sure you know how that works and that that's even good.
449
00:37:22,681.1738618 --> 00:37:29,911.1738618
And it's like, okay, now we can sort of go back through our gates because I know that integration piece because of my dummy test, at least that's feasible.
450
00:37:30,931.1738618 --> 00:37:33,661.1738618
Um, so unfortunately.
451
00:37:35,41.1738618 --> 00:37:40,321.1738618
Because of, you know, things like Murphy's Law and everything will definitely go wrong.
452
00:37:41,101.1738618 --> 00:37:43,861.1738618
Uh, you kind of have to attack it at all fronts at the same time.
453
00:37:44,71.1738618 --> 00:37:48,451.1738618
And if you want, like in the perfect world, you would, you would do that.
454
00:37:49,171.1738618 --> 00:37:52,51.1738618
Um, that's what raspberry pies are for.
455
00:37:52,771.1738618 --> 00:37:52,861.1738618
Yes.
456
00:37:53,536.1738618 --> 00:37:59,206.1738618
Yeah, I mean, yeah, I mean that's, it's, that's a great point.
457
00:37:59,236.1738618 --> 00:38:08,511.1738618
I mean, I think, um, you know, being able to abstract different v and v steps, like Mm-hmm.
458
00:38:08,686.1738618 --> 00:38:17,476.1738618
ultimately you need to get to the highest level of integration, but Right? back down and tearing it back to, okay, what's the most basic thing we can.
459
00:38:18,256.1738618 --> 00:38:18,586.1738618
Yeah.
460
00:38:20,176.1738618 --> 00:38:20,686.1738618
Yep.
461
00:38:20,986.1738618 --> 00:38:21,76.1738618
Yep.
462
00:38:21,76.1738618 --> 00:38:30,256.1738618
And I mean, if I can, if I can integrate this software stack onto this device and all it prints to the screen is hello world, at least we know, you know? Yeah, Yeah.
463
00:38:30,661.1738618 --> 00:38:30,991.1738618
right.
464
00:38:30,991.1738618 --> 00:38:31,621.1738618
Exactly.
465
00:38:31,681.1738618 --> 00:38:32,371.1738618
Exactly.
466
00:38:33,91.1738618 --> 00:38:35,521.1738618
Um, fascinating.
467
00:38:35,581.1738618 --> 00:38:36,571.1738618
Did you.
468
00:38:38,941.1738618 --> 00:38:42,181.1738618
So you talked a little bit about Agile, and Yeah.
469
00:38:42,421.1738618 --> 00:38:45,541.1738618
like a frequent prototyping kind of strategy.
470
00:38:47,341.1738618 --> 00:38:54,631.1738618
I'd love to hear a little bit about, um, kinda the people process and technology part of that, Okay.
471
00:38:55,771.1738618 --> 00:38:58,471.1738618
when you started to push that out.
472
00:39:00,271.1738618 --> 00:39:16,891.1738618
What sort of people side changes did you have to do? Were there, was there training? Uh, Did you, did you select were people selected for that program that had some exposure to Agile in the past? Like what did that look like? that's a good question.
473
00:39:17,371.1738618 --> 00:39:22,771.1738618
So it's always been more of a mentoring than a training thing in my experience.
474
00:39:23,851.1738618 --> 00:39:31,21.1738618
So what'll happen is there will be sort of your, you know, to borrow a term from, from my previous work.
475
00:39:31,51.1738618 --> 00:39:38,851.1738618
You'll have like your agile evangelist, so to speak, and this person will have enough of experience, at least operating at a scrum level.
476
00:39:38,881.1738618 --> 00:39:39,181.1738618
Okay.
477
00:39:39,181.1738618 --> 00:39:40,741.1738618
That person's done that before.
478
00:39:40,741.1738618 --> 00:39:47,281.1738618
So they'll be a scrum master and we're trying to bring on a new scrum into the release train.
479
00:39:47,866.1738618 --> 00:39:53,566.1738618
So we'll have to have that, that scrum master sort of mentor or participate with these other Scrum masters.
480
00:39:54,16.1738618 --> 00:40:01,456.1738618
And then of course we will have somebody who's had the most experience or specialized, um, I.
481
00:40:01,456.1738618 --> 00:40:10,486.1738618
wouldn't say training per se, but having sort of a additional exposure to certain agile material who also is quite familiar with being a scrum master.
482
00:40:10,486.1738618 --> 00:40:11,896.1738618
Maybe they'll be the RTE.
483
00:40:11,896.1738618 --> 00:40:14,626.1738618
And so then you'll sort of build that core.
484
00:40:15,751.1738618 --> 00:40:31,231.1738618
And then you'll take the people who have the most, um, exposure to like the subject matter experts or the, the team leads and like sort of older, um, team, organization, um, strategies that you have.
485
00:40:31,231.1738618 --> 00:40:35,971.1738618
The sort of the vestiges of those things that always kind of remain in my experience like.
486
00:40:36,826.1738618 --> 00:40:43,6.1738618
So you'll have team leads, but then you'll have someone who's like a, a product owner for whatever they're doing and that may or may not be the same person.
487
00:40:43,186.1738618 --> 00:40:49,141.1738618
And there's sort of a little bit of a confusion of the overlap, you know, um, Hmm.
488
00:40:49,546.1738618 --> 00:40:51,526.1738618
and how to best manage that.
489
00:40:51,736.1738618 --> 00:40:53,356.1738618
Um, so.
490
00:40:53,971.1738618 --> 00:41:09,961.1738618
When that mentoring then will kind of spread out from the scrum master, RTE core, and then from anybody like the more, uh, familiar or the more seasoned scrums will then have other people come in and set in on their sprint planning activities or different things like that.
491
00:41:10,741.1738618 --> 00:41:20,131.1738618
And so my experience, it's always been, yeah, more of a mentoring thing where you, you set that core up and then you have other people go basically touch, touch that core.
492
00:41:21,76.1738618 --> 00:41:23,146.1738618
It'll sort of spread out from there.
493
00:41:23,146.1738618 --> 00:41:24,46.1738618
From that exposure.
494
00:41:26,776.1738618 --> 00:41:28,156.1738618
Wow, that's, that's good.
495
00:41:28,246.1738618 --> 00:41:30,106.1738618
I mean that, it sounds pretty structured.
496
00:41:30,796.1738618 --> 00:41:31,396.1738618
Yeah.
497
00:41:32,446.1738618 --> 00:41:32,716.1738618
Yeah.
498
00:41:32,716.1738618 --> 00:41:44,81.1738618
In our, in our case, um, you know, without going into too many details, is there really is, um, it, it's really running smooth, you know, and of course there's always.
499
00:41:45,91.1738618 --> 00:41:52,51.1738618
You know, this, this team norming, forming, and storming, and you'll hire someone new or someone will leave and you'll have additional scrums.
500
00:41:52,51.1738618 --> 00:41:54,991.1738618
And that'll always, there's always that ripple.
501
00:41:54,991.1738618 --> 00:42:05,281.1738618
You know, someone just threw a rock in the pond kind of a thing, but it settles out pretty well if, if you, um, have the right? people in the, in the job and enough experience.
502
00:42:07,231.1738618 --> 00:42:07,621.1738618
Yeah.
503
00:42:08,671.1738618 --> 00:42:27,16.1738618
From a process perspective, or even like an IT ecosystem that's used to support, you know, this new way of doing things, does that look like? Any lessons learned there? Any good practices? I really like Azure DevOps, um, from my, from my experience actually.
504
00:42:27,16.1738618 --> 00:42:33,346.1738618
But that's because of, um, API availability, uh, from my experience.
505
00:42:33,346.1738618 --> 00:42:36,796.1738618
And this is, this is also not a knock on, on Jira either, by the way.
506
00:42:36,796.1738618 --> 00:42:38,26.1738618
So you could do the same thing.
507
00:42:38,56.1738618 --> 00:42:41,566.1738618
It's just I haven't, I haven't as much experience there.
508
00:42:42,586.1738618 --> 00:43:01,231.1738618
But, um, you know, one of the hardest parts, um, for being a product owner, which is something that, that I actually do at, at this, uh, juncture in my career that's fairly new, uh, is utilizing APIs so that I can just use AI integrations to make my work items, Mm-hmm.
509
00:43:01,906.1738618 --> 00:43:07,156.1738618
you know, so you can get the ideas where we'll have some discussions and take notes.
510
00:43:07,741.1738618 --> 00:43:14,491.1738618
Um, get a lot of thoughts for specific user stories, and now I have all those things kind of typed up and, and taken down.
511
00:43:14,491.1738618 --> 00:43:26,971.1738618
I can push them through a large language model into a DO and then go massage everything, because of course, you know, anytime you do anything with, with the large language model, the, um, the human in the loop is key.
512
00:43:27,91.1738618 --> 00:43:30,91.1738618
You know, because you're responsible for whatever it does.
513
00:43:30,691.1738618 --> 00:43:33,961.1738618
And so Yes, you had to go back and, and, and take care of that.
514
00:43:33,961.1738618 --> 00:43:35,581.1738618
But I found that, uh.
515
00:43:36,106.1738618 --> 00:43:45,706.1738618
From my earlier time as a human computer interface engineer building GUIs is that success is measured in, in, it's like playing golf with clicks.
516
00:43:46,6.1738618 --> 00:43:47,926.1738618
Actually, is is probably the best way to say it.
517
00:43:47,926.1738618 --> 00:43:53,626.1738618
Success is golf with clicks and, and in HCI you don't want very many clicks, right? So.
518
00:43:54,316.1738618 --> 00:43:54,586.1738618
Yeah.
519
00:43:55,141.1738618 --> 00:44:05,281.1738618
you can use, you can kind of get around, uh, and speed up agile planning by using large language models to get unstructured like stories written and leverage the, the API.
520
00:44:06,301.1738618 --> 00:44:07,126.1738618
So Hmm.
521
00:44:07,651.1738618 --> 00:44:17,161.1738618
it's a simple way to go about it, but it's a way less clicks if I can generate, you know, 30 stories that I just go have to peer review, then doing all that clicking.
522
00:44:18,496.1738618 --> 00:44:18,946.1738618
Yep.
523
00:44:19,156.1738618 --> 00:44:19,876.1738618
Absolutely.
524
00:44:19,876.1738618 --> 00:44:20,116.1738618
Yeah.
525
00:44:20,116.1738618 --> 00:44:21,286.1738618
Makes, makes ton of sense.
526
00:44:21,556.1738618 --> 00:44:27,61.1738618
And, and that's a really good segue to talk more deeply about ai, Okay.
527
00:44:27,251.1738618 --> 00:44:27,541.1738618
Yeah.
528
00:44:27,766.1738618 --> 00:44:28,246.1738618
engineering.
529
00:44:28,906.1738618 --> 00:44:34,186.1738618
Um, how, Hmm.
530
00:44:35,951.1738618 --> 00:44:38,116.1738618
We actually just ran a survey on this.
531
00:44:38,391.1738618 --> 00:44:38,681.1738618
Okay.
532
00:44:38,956.1738618 --> 00:44:45,16.1738618
the way that we, we ask the question in the survey is.
533
00:44:46,696.1738618 --> 00:44:51,471.1738618
How many hours a week are you using AI Oh.
534
00:44:51,676.1738618 --> 00:45:00,661.1738618
with software development? I'd love to get your perspective and then I can give you some of what we're seeing That sounds good.
535
00:45:00,691.1738618 --> 00:45:00,931.1738618
Okay.
536
00:45:00,931.1738618 --> 00:45:01,861.1738618
Yeah, I like that.
537
00:45:01,891.1738618 --> 00:45:15,41.1738618
Um, that's a really good question because, uh, the, it, there's various degrees of integration, you know, levels of maturity that you might have with your, like.
538
00:45:16,66.1738618 --> 00:45:28,396.1738618
Even your developer environment, you may have a, like a client code or a kilo code or a cloud code or codex or something really integrated super deeply into just your workstation, so to speak.
539
00:45:29,506.1738618 --> 00:45:37,126.1738618
Um, and so I would say I'm a product owner now, so my coating level has kind of gone down.
540
00:45:38,521.1738618 --> 00:45:41,911.1738618
I probably only code probably 25% of my time.
541
00:45:41,911.1738618 --> 00:45:45,871.1738618
And a lot of it is that people work at this point that we were talking about earlier.
542
00:45:46,111.1738618 --> 00:45:55,231.1738618
Um, of that, you know, when I have a user story, I always start with AI enabled assistance.
543
00:45:55,771.1738618 --> 00:46:01,351.1738618
And part of the reason is because of background I've had in like systems engineering.
544
00:46:02,446.1738618 --> 00:46:09,256.1738618
Prompt engineering and also just like context engineering more broadly is, uh, or even, yeah.
545
00:46:09,256.1738618 --> 00:46:18,376.1738618
And then let's add to that, you know, the fact that I've been a, a technical writer and written scholarly articles and all kinds of things is I'd start with that prompt engineering piece.
546
00:46:19,546.1738618 --> 00:46:27,16.1738618
Um, and so I can actually write out requirements from that whole collection of prior experiences into my prompt and my system prompts.
547
00:46:27,16.1738618 --> 00:46:27,676.1738618
And I'm, I'm pretty.
548
00:46:28,381.1738618 --> 00:46:31,801.1738618
Skill that leveraging both of those things to work together.
549
00:46:31,801.1738618 --> 00:46:38,341.1738618
You know, I typically, uh, when I teach people about prompting, I'll tell them that this system prompt is like absolutely key.
550
00:46:38,731.1738618 --> 00:46:42,601.1738618
Um, that this is how you kind of lean over and whisper to the machine.
551
00:46:42,601.1738618 --> 00:46:45,781.1738618
It's like, I'm about to ask you this question, but you're gonna answer like this.
552
00:46:46,951.1738618 --> 00:46:56,491.1738618
And, and if you leverage that really, really well, then you get the output that you need and you have encoded the requirements and you have this fantastic starting point.
553
00:46:56,581.1738618 --> 00:47:13,831.1738618
And it's really, really difficult in software engineering, um, in, in my opinion, to justify not doing that because I can now use my natural language as a high, high level language to generate high level language code that if I'm good at coding.
554
00:47:14,401.1738618 --> 00:47:24,211.1738618
Will compile down to see, because I'm using the right libraries in that high level language and I still get all of that, uh, C benefit and speed.
555
00:47:24,661.1738618 --> 00:47:31,416.1738618
But now I also have the speed benefit of being able to take my ideas and my native language and have all that work with the computer.
556
00:47:33,181.1738618 --> 00:47:34,981.1738618
Hmm, really fascinating.
557
00:47:37,861.1738618 --> 00:47:45,511.1738618
in the results from the study, think it's around 25% are using it eight plus hours a week.
558
00:47:46,606.1738618 --> 00:47:47,86.1738618
Okay.
559
00:47:47,176.1738618 --> 00:47:47,656.1738618
Okay.
560
00:47:47,701.1738618 --> 00:47:51,856.1738618
a, a, a full day out of the five or, you Mm-hmm.
561
00:47:53,176.1738618 --> 00:47:55,636.1738618
I think that, standard work week, how Yeah.
562
00:47:55,636.1738618 --> 00:47:55,936.1738618
Yeah.
563
00:47:55,936.1738618 --> 00:48:02,986.1738618
I think that jives, you know, because if I, if I kind of always start from there and I code 25% of the time, you know, that's about right.
564
00:48:03,676.1738618 --> 00:48:04,576.1738618
Yeah, Yeah.
565
00:48:04,996.1738618 --> 00:48:05,596.1738618
I'd agree.
566
00:48:05,746.1738618 --> 00:48:13,6.1738618
another, um, roughly 50% are in that three to four, maybe five, six hour range.
567
00:48:13,996.1738618 --> 00:48:18,581.1738618
So I mean, it's, you know, it is being used pretty, I.
568
00:48:19,411.1738618 --> 00:48:20,671.1738618
In a pretty widespread way.
569
00:48:20,671.1738618 --> 00:48:25,411.1738618
And I think it, it's interesting to compare that to, on the hardware side of things.
570
00:48:25,411.1738618 --> 00:48:25,801.1738618
I Mm-hmm.
571
00:48:25,831.1738618 --> 00:48:30,601.1738618
it's being used nearly as much, No, I would agree.
572
00:48:30,991.1738618 --> 00:48:31,381.1738618
Yeah.
573
00:48:31,381.1738618 --> 00:48:34,651.1738618
And, and part of it, part of it is, um.
574
00:48:36,106.1738618 --> 00:48:44,146.1738618
Data scientists and software engineers and AI ML people came for their own jobs first because it's what we understood.
575
00:48:44,656.1738618 --> 00:48:57,46.1738618
Um, and the, the degree of structure and l LMS being particularly good at, at paying attention to certain things, and then transforming the one, you know, A to B.
576
00:48:58,246.1738618 --> 00:49:01,156.1738618
uh, IT software is just a natural target for that.
577
00:49:01,156.1738618 --> 00:49:09,16.1738618
And then of course, software is magic in that it can basically be written down like a spell book and become effective, you know, it's something that.
578
00:49:09,16.1738618 --> 00:49:16,456.1738618
you're writing out like ruins on your computer screen, you know, So it, there's, there's a high value target there.
579
00:49:17,266.1738618 --> 00:49:25,606.1738618
And so you start with that, and then you find that by using, you know, software to integrate AI into certain processes, then maybe I can take.
580
00:49:26,446.1738618 --> 00:49:42,616.1738618
The, uh, the process by which we go through some sort of hardware development or even going through supplier SALs or different documents that that will, uh, or generating requirements or things like that for, and, and scoring those things.
581
00:49:43,276.1738618 --> 00:49:54,886.1738618
There's a bunch of different things like that that one could do that in fact, that will, uh, impact the degree at which we can build our hardware or we can do, you know, certain documentation activities or, and all that stuff.
582
00:49:55,786.1738618 --> 00:50:19,936.1738618
Um, you know, I've, I've also we're a little bit removed from being able to like, say, use an LLM to program a robot in, to short circuit the construction of, you know, some diff some, um, not difficult but like complex, uh, device that would then later get integrated into like an aircraft or something like that.
583
00:50:19,936.1738618 --> 00:50:24,676.1738618
Uh, and that goes back to what I really, um, I remember like Sisyphus.
584
00:50:25,591.1738618 --> 00:50:35,251.1738618
Who rolls the stone up, right? And his, his, uh, torture in, uh, Hades is to roll the stone up and can never quite get it all the way to the top of the hill and just rolls back down.
585
00:50:35,251.1738618 --> 00:50:36,961.1738618
So he's gotta go back and push it back up.
586
00:50:37,681.1738618 --> 00:50:39,991.1738618
And in our lives, that's the dishes in the laundry.
587
00:50:41,221.1738618 --> 00:50:44,761.1738618
And until the robots can do the dishes or the laundry, AI has not helped us.
588
00:50:45,286.1738618 --> 00:50:55,231.1738618
Uh, 'cause as at this point in time, what it really does is it automates, uh, you know, creativity and a lot of, you know, let me generate, let me generate images, for example.
589
00:50:56,71.1738618 --> 00:51:10,651.1738618
Um, and so there's, it's one of those things that, because images can be generated and done on a computer that doesn't have arms and legs, the, the, the first things that we're clipping out is the stuff that we actually probably should be doing to refresh our minds and to.
590
00:51:11,551.1738618 --> 00:51:25,981.1738618
Strengthen that right brain view of the world because it's, I think it, it's my opinion that the left brain is what destroys the world, and so it has to be balanced by the right brain in order to, you know, have a flourishing human existence and AI is not gonna help us do that.
591
00:51:27,361.1738618 --> 00:51:27,991.1738618
Yeah.
592
00:51:28,111.1738618 --> 00:51:30,781.1738618
No, that's really good perspective.
593
00:51:30,841.1738618 --> 00:51:32,581.1738618
I didn't know we were gonna be this philosophical.
594
00:51:32,581.1738618 --> 00:51:35,896.1738618
I love, I love the threads, the themes Thank you.
595
00:51:35,911.1738618 --> 00:51:36,151.1738618
though.
596
00:51:36,151.1738618 --> 00:51:37,171.1738618
That's, that's great.
597
00:51:37,171.1738618 --> 00:51:39,841.1738618
This is, this is the way I typically am.
598
00:51:39,841.1738618 --> 00:51:50,281.1738618
This is, you know, um, whenever I do podcasting or writing or anything like that, um, you know, like the, the Faithful Fireside podcast that I was mentioning to you before.
599
00:51:50,281.1738618 --> 00:52:01,441.1738618
It's just, this is sort of what I try and do is how do I connect work and, and AI and tech an experience that, that we're all going through to everything.
600
00:52:02,41.1738618 --> 00:52:04,141.1738618
I, I, I don't really like to.
601
00:52:05,296.1738618 --> 00:52:06,436.1738618
Compartmentalize.
602
00:52:06,436.1738618 --> 00:52:18,466.1738618
And I think we have a really strong, um, there's a lot that encourages us to do that, but there's, I think there are a lot of patterns that breach, or I shouldn't, I don't know if I wanna say breach that sounds more aggressive.
603
00:52:18,466.1738618 --> 00:52:25,996.1738618
They, they bridge or they span, you know, more of our, um, modes of existence.
604
00:52:25,996.1738618 --> 00:52:30,496.1738618
And so I think it's nice to just kind of let that happen and, and see what we notice.
605
00:52:32,86.1738618 --> 00:52:32,506.1738618
Yeah.
606
00:52:32,506.1738618 --> 00:52:33,616.1738618
No, that's fascinating.
607
00:52:33,616.1738618 --> 00:52:43,531.1738618
I mean, we're the same person whether or not you're at home playing with your kids or you go to work and you're doing serious, serious stuff, That's right.
608
00:52:43,531.1738618 --> 00:52:43,636.1738618
That's right.
609
00:52:46,186.1738618 --> 00:52:46,726.1738618
yeah.
610
00:52:46,756.1738618 --> 00:52:47,146.1738618
Yeah.
611
00:52:47,176.1738618 --> 00:52:48,466.1738618
Good, good, good, good.
612
00:52:52,501.1738618 --> 00:52:55,36.1738618
Going back to this study, Yeah.
613
00:52:55,531.1738618 --> 00:53:07,826.1738618
it was one of the things that really surprised me is there was a large percentage of respondents that worried about the or security of the code that AI generated, Hmm.
614
00:53:08,11.1738618 --> 00:53:09,481.1738618
and then we asked the question.
615
00:53:10,546.1738618 --> 00:53:16,486.1738618
How much are you reviewing that code? And they did not match up.
616
00:53:16,686.1738618 --> 00:53:16,976.1738618
Yeah.
617
00:53:16,996.1738618 --> 00:53:18,916.1738618
there was a high level of concern.
618
00:53:18,946.1738618 --> 00:53:19,236.1738618
Yeah.
619
00:53:19,546.1738618 --> 00:53:23,71.1738618
But then there was a low level of review Yes.
620
00:53:23,206.1738618 --> 00:53:23,716.1738618
it varied.
621
00:53:23,716.1738618 --> 00:53:26,146.1738618
Right? We're gonna cross tab that and see what it looks like.
622
00:53:26,146.1738618 --> 00:53:27,136.1738618
We haven't Sure, but.
623
00:53:27,191.1738618 --> 00:53:27,481.1738618
sure.
624
00:53:28,136.1738618 --> 00:53:35,391.1738618
Um, well, so, and, and I think this kind of speaks to your earlier point, which is we're all dead busy Yeah.
625
00:53:35,426.1738618 --> 00:53:40,766.1738618
we can recoup a little time, we're gonna take the win and keep sprinting.
626
00:53:41,321.1738618 --> 00:53:42,171.1738618
Yeah, yeah.
627
00:53:42,356.1738618 --> 00:53:45,56.1738618
but I'd love to get your perspective on that as well.
628
00:53:45,376.1738618 --> 00:53:47,956.1738618
Yeah, I think that that's a great, that's a great question.
629
00:53:47,956.1738618 --> 00:53:51,556.1738618
There's something about humans, you know, it just.
630
00:53:52,786.1738618 --> 00:53:57,196.1738618
We'll know we're not supposed to eat the apple, you know, so to speak.
631
00:53:57,196.1738618 --> 00:53:58,546.1738618
And then we will do it anyway.
632
00:53:58,906.1738618 --> 00:54:08,266.1738618
Or every single fairy ster, every, uh, fairytale or anything you've ever told your kids, the, the hero will get warned, you know, Hey, don't go do that thing.
633
00:54:09,76.1738618 --> 00:54:12,946.1738618
And so naturally, the very first thing they go do is that exact thing.
634
00:54:12,946.1738618 --> 00:54:13,741.1738618
And thing.
635
00:54:13,876.1738618 --> 00:54:15,646.1738618
because if they didn't, there wouldn't be a story.
636
00:54:15,646.1738618 --> 00:54:17,326.1738618
Right? But that, but that's the way that.
637
00:54:18,361.1738618 --> 00:54:21,241.1738618
That's just the way that this works and so everybody can kind of see.
638
00:54:21,241.1738618 --> 00:54:21,871.1738618
It's like, okay.
639
00:54:21,871.1738618 --> 00:54:22,201.1738618
Yeah.
640
00:54:22,201.1738618 --> 00:54:28,96.1738618
So when I generate the code, you know, I get this massive ROI ON on time and even.
641
00:54:28,936.1738618 --> 00:54:39,616.1738618
Yeah, If I'm very conscientious about prompt engineering, I can actually say, Hey, generate this Python script and use a callback architecture that's rule-based that will then trigger these certain callbacks.
642
00:54:39,616.1738618 --> 00:54:47,146.1738618
And that's really easy to extend by adding another callback to call and then it just say, Hey, tell me how long processing was.
643
00:54:47,146.1738618 --> 00:54:49,576.1738618
Add a timer callback, call it right at the very end.
644
00:54:50,986.1738618 --> 00:54:51,736.1738618
And you can do that.
645
00:54:51,736.1738618 --> 00:54:58,246.1738618
So you can use AI and you can even tell it, Hey, we're gonna use really good design patterns when we go by and you know, go about.
646
00:54:58,666.1738618 --> 00:54:59,566.1738618
Doing this.
647
00:55:00,466.1738618 --> 00:55:04,216.1738618
Um, but most people don't because that takes a little bit more effort and a little bit more thought.
648
00:55:04,216.1738618 --> 00:55:09,406.1738618
And you typically have to have software engineering experience to, to think in that, that way.
649
00:55:09,406.1738618 --> 00:55:16,606.1738618
So it's not like the machine will necessarily do everything, but we, we get that time, we have this fear.
650
00:55:17,146.1738618 --> 00:55:24,166.1738618
Um, and then, you know, when all set is and is done, sort of at the end of the day, it's like, well, I generated that code.
651
00:55:25,201.1738618 --> 00:55:27,871.1738618
Um, and I got a week's worth what would've taken me a week.
652
00:55:27,871.1738618 --> 00:55:32,371.1738618
I got done in like an hour, two hours, half a day.
653
00:55:32,371.1738618 --> 00:55:37,471.1738618
And then instead of taking that extra time that I just got to go back and review.
654
00:55:38,581.1738618 --> 00:55:41,791.1738618
Uh, because one thing you don't necessarily get right, um.
655
00:55:42,286.1738618 --> 00:55:47,176.1738618
With this generated code as an ROI is quality, it's not, it's not quality code.
656
00:55:47,176.1738618 --> 00:55:49,996.1738618
The, the best coders on our team can always take what I generate.
657
00:55:49,996.1738618 --> 00:55:57,586.1738618
I can give it to them and then they will make the code per more performant or, you know, it, it'll work, but they will actually improve it somehow.
658
00:55:57,616.1738618 --> 00:56:02,866.1738618
'cause we have some really great developers on our team and, um.
659
00:56:03,391.1738618 --> 00:56:10,321.1738618
But instead of just like taking that win and then using that extra time for the review is we, now I have to shove more work through my pipe.
660
00:56:10,621.1738618 --> 00:56:12,181.1738618
The, we change the expectation.
661
00:56:12,961.1738618 --> 00:56:19,171.1738618
There's still 24 hours in a day, but now you're expected to do 72 hours of work in it, you know, so to speak.
662
00:56:19,861.1738618 --> 00:56:21,331.1738618
And so we've always done that.
663
00:56:21,901.1738618 --> 00:56:30,541.1738618
Uh, every time we make technology, you know, now I don't have to go out with the washboard and hang my clothes with the clothes pins, and I have washers and dryers and all that stuff.
664
00:56:31,606.1738618 --> 00:56:33,466.1738618
I just filled that time with something else.
665
00:56:33,466.1738618 --> 00:56:40,756.1738618
There's just something about us who we struggle to set in silence and um, you know, take that extra rest.
666
00:56:40,756.1738618 --> 00:56:44,776.1738618
It's like instead we're just gonna blow ourselves up some more.
667
00:56:44,776.1738618 --> 00:56:47,511.1738618
So it is always just more work is gonna get pushed down that pipe.
668
00:56:49,696.1738618 --> 00:56:50,206.1738618
Yeah.
669
00:56:51,406.1738618 --> 00:56:51,826.1738618
Yeah.
670
00:56:52,306.1738618 --> 00:56:55,306.1738618
Uh, that, that is definitely a consistent challenge.
671
00:56:55,366.1738618 --> 00:56:57,136.1738618
Um, for sure.
672
00:56:57,856.1738618 --> 00:56:58,576.1738618
yeah.
673
00:56:58,696.1738618 --> 00:56:59,566.1738618
it's disappointing.
674
00:57:00,286.1738618 --> 00:57:02,281.1738618
But it is, Yeah.
675
00:57:02,401.1738618 --> 00:57:02,851.1738618
Yeah.
676
00:57:03,271.1738618 --> 00:57:04,681.1738618
Yes, absolutely.
677
00:57:05,41.1738618 --> 00:57:08,71.1738618
Um, okay, last question.
678
00:57:08,701.1738618 --> 00:57:20,56.1738618
Uh, if you could redesign anything in the world, say like the better mousetrap, oh what, what would it be for you? wow.
679
00:57:21,16.1738618 --> 00:57:23,956.1738618
Now that, that's a really good question.
680
00:57:24,316.1738618 --> 00:57:24,826.1738618
Um.
681
00:57:27,151.1738618 --> 00:57:38,341.1738618
Anything in the world to make it be the better mousetrap, this probably exists, but if it does, I don't know.
682
00:57:39,181.1738618 --> 00:57:46,591.1738618
Um, but I would actually like to be able to have a typewriter.
683
00:57:47,461.1738618 --> 00:58:10,441.1738618
This is the one Internet of Things thing I actually want is a typewriter that as I write and it types to the page, it's also sending what I'm typing to a file on a computer so that I can have the, the mechanical experience of operating a typewriter and, and seeing what I'm writing being made right in front of me, and I can be away from a screen.
684
00:58:11,371.1738618 --> 00:58:25,291.1738618
And so there's, there, I, I think screens do things to us with our attention span and, um, you know, the way that they, the notifications go off and you have these constant, like a attention jerks, so to speak.
685
00:58:25,471.1738618 --> 00:58:32,401.1738618
Even, even when you're on your work computer and someone's, you know, messaging you on teams or Slack or whatever it is that you're using, Yeah.
686
00:58:32,431.1738618 --> 00:58:33,631.1738618
and it just ping, ping, ping.
687
00:58:33,631.1738618 --> 00:58:39,241.1738618
I don't want it to see that I'm, I'm writing whatever I wanna write, but I also need that thing to be.
688
00:58:39,646.1738618 --> 00:58:46,846.1738618
Stored in, you know, some server somewhere so that I don't have to go and scan all this stuff after the fact.
689
00:58:46,846.1738618 --> 00:58:49,336.1738618
'cause it, if it adds that many steps, I'll never use it.
690
00:58:50,566.1738618 --> 00:58:56,986.1738618
So somebody's probably already made that, but I would, that's the, that's the one thing I would probably want is the, the IOT typewriter.
691
00:59:00,916.1738618 --> 00:59:01,606.1738618
I like that.
692
00:59:01,606.1738618 --> 00:59:02,86.1738618
I like that.
693
00:59:02,86.1738618 --> 00:59:14,201.1738618
I mean, you're, I think this speaks to a larger point around attention and how hard it is to own your own attention unless you put up.
694
00:59:15,691.1738618 --> 00:59:21,256.1738618
You know, some barriers or controls, around Yes, yes.
695
00:59:21,331.1738618 --> 00:59:21,601.1738618
that a lot.
696
00:59:21,976.1738618 --> 00:59:22,306.1738618
Yeah.
697
00:59:22,306.1738618 --> 00:59:37,426.1738618
It's, it's, you know, unfortunately the attention is, um, monetizable now, right? Ad revenue and, and counts of clicks and, you know, Facebook.
698
00:59:38,186.1738618 --> 00:59:39,391.1738618
All those sorts of things.
699
00:59:40,261.1738618 --> 00:59:56,251.1738618
And so because you, because those free services, what they're really doing is they're selling you, is you have to try and figure out how to get only the best out of 'em you can get without rotting your own mind.
700
00:59:57,596.1738618 --> 01:00:01,891.1738618
And iot typewriter is one thing that I think might help with that.
701
01:00:03,526.1738618 --> 01:00:04,216.1738618
Yeah.
702
01:00:04,906.1738618 --> 01:00:05,506.1738618
like it.
703
01:00:05,566.1738618 --> 01:00:06,136.1738618
I like it.
704
01:00:06,346.1738618 --> 01:00:06,856.1738618
All right.
705
01:00:06,856.1738618 --> 01:00:07,486.1738618
Well, Dr.
706
01:00:07,486.1738618 --> 01:00:09,466.1738618
Rider, thank you so much for your time today.
707
01:00:09,466.1738618 --> 01:00:10,696.1738618
I greatly appreciate it.
708
01:00:11,116.1738618 --> 01:00:13,366.1738618
Um, wonderful conversation.
709
01:00:13,531.1738618 --> 01:00:14,851.1738618
Thank you very much, sir.
710
01:00:15,16.1738618 --> 01:00:15,376.1738618
much.
711
01:00:15,481.1738618 --> 01:00:16,531.1738618
Yes, it was great.
712
01:00:17,131.1738618 --> 01:00:17,521.1738618
Anytime.
713
01:00:18,911.1738618 --> 01:00:19,846.1738618
Uh, all right.
714
01:00:19,876.1738618 --> 01:00:20,476.1738618
Excellent.
715
01:00:20,506.1738618 --> 01:00:23,806.1738618
All right, well, everybody listening or watching, uh, thanks for your time as well.
716
01:00:24,136.1738618 --> 01:00:26,986.1738618
Uh, make sure to turn, uh, tune into the next episode.

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