Ep 69: The AI Hype Cycle - Where We Really Are

Watch the YouTube video version above or listen to the podcast below!

Episode Summary

This episode explores the AI hype cycle and its intersection with corporate reality, job markets, productivity tools, and leadership paradigms.

Alex Pokorny kicks off by questioning where we currently sit on the hype vs. reality curve. The team agrees that AI optimism is meeting grounded skepticism, especially as more professionals test tools and find their limitations.

Dave Dougherty notes how generational differences in career stages affect AI adoption—senior professionals embrace AI for productivity, while junior job seekers face a shrinking entry-level job market, potentially driven by AI's impact. He references a Stanford study noting a 13% decline in hiring for AI-exposed roles.

Ruthi Corcoran adds that uncertainty—stemming from both AI and broader macroeconomic pressures like tariffs—is leading corporations to pause hiring. However, organizations universally acknowledge AI's transformative potential. She observes increasing skepticism toward new AI tools, and highlights how Microsoft's Copilot, although not hyped, is steadily delivering incremental value.

The crew discusses case studies from major companies:

• Target is adopting AI to augment rather than replace workers.

• U.S. Bank is testing campaigns using synthetic audiences, albeit with limitations around cultural nuance.

• Amazon AWS is hiring more humans to ensure fresh thinking, acknowledging that AI can't inject new perspectives.

They also reflect on leadership self-awareness and bias in the age of AI. Many leaders are distant from ground-level work, making them poor judges of both customer needs and execution challenges.

Alex and Ruthi raise the difficulties in implementing AI tools across non-standard enterprise environments, where internal systems and processes are heavily customized. They discuss the need for an application layer between LLMs and company-specific workflows.

The episode concludes with a powerful insight: the skill that matters most now is learning agility—not what you know, but how quickly you can learn. The group emphasizes curiosity, experimentation, and a willingness to be uncomfortable as key traits in navigating the AI era.

Ep 69: The AI Hype Cycle - Where We Really Are Podcast and Video Transcript

[Disclaimer: This transcription was written by AI using a tool called Descript, and has not been edited for content.]

Dave Dougherty: Hello, welcome to latest episode of Enterprising Minds. We've got the whole crew here. We're going to talk about some AI use cases to try to bring the theoretical home to use. Things but also make fun of AI too with some of the image prompts if I'm understanding what was pitched before we hit record.

Alex, per you, I will kick it over to you to introduce your topic and then we will discuss.

Discussing the AI Hype Cycle

Alex Pokorny: Greater topic really is kind of the AI hype cycle and I, it's just a kind of a current feeling. I wanted to get your guys kind of temperature reading of where do you feel hype cycle versus reality is today?

Because where we started was ridiculous and I've been thinking about like the hype cycle versus the true reality as two curves. You got this way up in the sky-high curve, which is the hype, and then you got the bottom on the ground. Reality and occasionally they touch, but then something new comes out, agent, iGen, and then it bounces off again, and then you know, something else happens.

Image nano banana announcement bomb. Then it bounces off again and then it's we saw that with VEO, we saw that with any of the kind of video creation pieces. We saw that with some of the different launches of new tools. Of new models. There was also though. Like some areas that it just seemed like it was information fatigue of yeah, there's always a new AI model, and that was about the same time.

There was like database announcements and stuff like that, which were boosting things again.

Right now, I'm feeling like the bounces on a low. Low curve, where the hype cycle is starting to hit reality again, where people have been using it and they first thought they were going to lose their jobs and then they realized, oh, can write me a term paper, like that's not going to do my job anytime soon.

Things like that. Left come home. At least that's my feeling. I'm surrounded by a lot of people who do this kind of stuff all day, every day. So, it could be also that we've just had enough on the ground experience that we're already at that point. What do you guys feel and what do you think, like the people you work with, you feel like they're at that point or are they still on the hype cycle, like high bounce?

Dave Dougherty: I'm of two minds, so first thing I've been noticing, not just in the marketing side of things, but I think also in the financial side of things, we're getting to that point of. The hype cycles in reality meeting. There was a lot of talk on a lot of the financial news stuff that I listened to where, are we in an AI bubble?

Are we in an AI bubble? Maybe not, but a lot of these companies are financing their investments in chips and infrastructure with cash on hand, right? Which is a big difference from some of the other. Crashes and bubbles. But granted, I'm not a, I'm not a big finance person, so go listen to those for yourself.

But then with the marketing peers and sides of things I do think that

there, it's hitting at two different levels. One is. For more senior people or people more established in their careers. It's definitely AI's helping and I'm able to use it in these use cases to help me solve the problems that I have. However, for junior level. I've been talking to some people in university students across this year and a lot of them have either been applying to jobs or trying to find internships or just trying to get any kind of experience and they're not finding anything, which for me has always been okay.

Go. Start your own thing, go help a friend, start a thing. Get any experience you possibly can, volunteer with some nonprofit or just get involved and don't, don't charge somebody for something that they're doing right. Just to get the experience and build, what you have.

And I was at a conference at the University of Minnesota last week, and one of the professors brought up a study at Stanford that spoke about of the entry level jobs that had exposure to ai. There is a 13% decline in hiring and availability. For those jobs. So, it's definitely, it's a yes and for me

answering the other part of your question, and I think we should maybe, pick these apart a little bit after Ruthie says her brilliant part my coworkers, I'm not afraid of them at all. And like with them I'm not experiencing a lot of conversations around ai. Many of them are so focused on just getting things done and wrapping up the year that they're not actually incorporating things or looking for new ways to do things.

Or maybe they've thought about it, but they're not implementing it. Like I've offered to host some AI breakfast, which, I think. Could shake some people out of apathy, but at the same time it's just that, it's that time of year you hit Thanksgiving for a lot of people and it's okay.

My brain's going to start shutting down. because really, honestly, there's only three more weeks of this year. So that could just be a seasonal effect or a sample size problem. But Ruthie, what's your experience with where we are now?

Corporate Perspectives on AI Integration

Ruthi Corcoran: I am going to poll first on a little thread you left out there around the new hires.

Which I think it's an interesting dynamic going on. So, I see two things going on at in this space. One is everybody can see the potential with ai. They just don't know exactly how we're going to realize it. And I'm speaking from like a corporate big enterprise sort of perspective.

So, the question mark is the how not the weather AI is going to have an impact. And so, I can see that putting. Creating a pause in the hiring, right? You don't want to be hiring and continuing to bring on new people. If you think that the way you're doing business is going to shift and you're going to have to reallocate resources, so that dynamic could be at play.

The other one that I think is really important is yeah, tariffs. Tariff headwinds are a big deal. And what do you do when you have the potential for a lot of extra costs in doing business, but you're not exactly sure? There's a lot of uncertainty. You pause, you say we're not going to be expanding because we don't know what's coming in the next few months.

We don't know what kind of retaliatory tariffs are going to come up. We don't know what our supply chains are doing. So, I, there's a lot of uncertainty. And so I guess your comment about, there being a drop in hiring of new people within the information working space just lends one more data point to this I guess hypothesis I have out there, which is we've got a lot of uncertainty going on in the market, which is making companies just want to wait, let's pause.

And that plays into this like hype cycle, which I is, I think the fact that there is a hype cycle. Also is another data point in this uncertainty piece. We don't know. We don't know where it's going. It's if you go back to when airplanes were first invented, everybody could see that this is going to change a lot.

It was going to change everything, but you don't exactly know where it's going. And it ultimately did transform the entire world and how we do things. But you couldn't necessarily point to this is the one company or the one sort of killer app within it that was going to pay off in the long run. You could just see the change and you know it's going to be transformative.

Within the specific hype cycle, one thing that I've seen very one thing I've seen that has evolved over the last few months is. The level of skepticism of new tools or new apps that are going to just change everything, like the skepticism of those have gone up, right? Everybody looks at a new tool, and they go, yeah, but really and tell me about the let's get into the details and how much work needs to be put in for it to do.

The thing you showed in the demo, does it do the thing in the demo every single time? And so, I think. Our realistic expectations are catching up to what were hype level expectations around what the new fancy technology is. But then where I've seen like a steady increase in, okay, this is something that I can be using on the day to day, is more in the sort of copilot productivity suite where it's slowly, incrementally getting better.

And no one had really high expectations of copilot to begin with. They're just like, oh, this is the corporate tool I have to use. And so now it's there wasn't a hype cycle with copilot because it is a Microsoft tool. It's probably going to be good, but it's been continuously increasing. And so those are, I when you're talking about the hype cycle versus sort of the realistic on the ground, that's the picture I have in my head of like copilots, like doing this.

The hype cycle is. Maybe gradually going down. And then the last little observation, I'll just chime in on this topic too, is at the, we can, you mentioned the potential for a bubble. At least we're seeing the hype cycle happen in stock prices. So, if you look at the Vanguard Technology Fund, it hit 800 at the beginning of the month, and I went.

That seems high. And sure, enough it's dropped to, the seven twenties in the last couple weeks. And so, I wonder if we're also seeing the hype cycle play out a little bit just overall.

Challenges and Realities of AI Implementation

Alex Pokorny: Yeah, I think that skepticism is an interesting point because that's skepticism comes from the unmet hype. And then you start to gain this skepticism, which kind of like tampers it down. So, others may be excited, but you're less excited. Like you're pulling that top curve back down to reality.

Might be even past, going too far, potentially, but you have that kind of experience now that you've seen this, you've seen it for a while. You kind of have your arms around it and your hands around it enough that you can get some idea of this new announcement, I'm going to question some aspects of it and this new next new announcement that's so far, so amazing.

I don't know maybe it'll live up to the demo, which before it was, this is amazing. I've never seen anything like this. It's hard to have, any kind of marker against it. And then it's oh, it probably could do those things.

Dave Dougherty: I wonder how much Slack has ruined new tools for everybody.

I pick on Slack because of just the insane number of notifications that you get with that, whereas we're going to improve efficiency of communication. No, you didn't. You just, you're blowing up my phone for every little thing and I don't, I'm not here for that, yeah, I think it was interesting going to that that Ignite conference and if you'd subscribe to the.

Pathways newsletter, you would've seen the latest notes on that for a deeper dive. But one of the things that stood out to me on one of the panels was they were asking some rather large organizations like, hey, how are you doing ai? How are you leveraging it? And the use cases were very dependent on the organization.

For example, the woman from Target, I forget her name, but she. Was speaking about how they have an approach of collaborator, nor was it collaborator, not an enemy or not a replacement. Like they are specifically looking on how to augment not yeah. The how to augment their workforce so that they can continue to do what they need to do and maybe come up with some new, interesting ideas for the problems that they work on, but they're not looking to replace their workforce.

The US bank, CMO, for example, brought up the fact that they too are using it to address the problems that they typically have to face. But more interestingly, they are creating synthetic audiences to test campaigns and marketing messages against prior to launching them. So, if you're going to be targeting mortgages to under thirties.

How do you do that? And he said the draw, it's been working well, but the main drawback of it is that it is really bad at specific cultural nuances. So, you still need humans in the loop to better understand that target audience, to make sure that you're not inadvertently, angering them with some, side example or side comment.

Then there was this guy from Amazon who, when you think of automation and replacing people, you would think it would be coming from Amazon, right? He was specifically AWS, but he said that their stance was, while they are leaning hard into automation, they are specifically looking at hiring new people because AI cannot bring fresh perspectives to the problem solving.

And they are hiring new people specifically to have fresh approaches to the problems that they have so that they're not just relying on the old timers who tackle problems in similar ways because of how they came up in their careers or the situations they faced that may or may not be pertinent to what's going on now.

So, across those three, I thought that was a really interesting cross section of how major organizations are. Approaching these problems. And I think, a lot of the organizations that I've heard of or that I've talked with are somewhere in the middle of these, or they're still trying to figure out use cases for these.

What are your thoughts on this sort of outline?

Ruthi Corcoran: I cool thought on the Amazon piece too, because. I'm sure we've all observed this as you're prompting and you're asking questions or sharing your perspective and trying to get feedback, there's still a bias to say Yes. Great point.

Like that. That's always the introduction is some sort of, it drives me nuts. Re reaffirming what you're doing, right? Yeah. It drives me nuts as well, so I've tried to make tweaks as well to just get that to shift. It's no, I don't. I'm not so tied to the way I think, right? So, I can see where the gentleman from Amazon is coming from, of if we are seeing that our interactions with AI are just reinforcing our current way of working.

Maybe they're making us better and more productive, but they're like within the same stream. Introducing new streams is going to be really beneficial. The other thought I was thinking about with the comment from Target about collaborate or not replacement is, the incentive for a manager. So, let's set aside like company organization for a moment and sort of reorgs and reallocation of resources.

If we think about the incentive from the manager's perspective, it's not really, at least not yet to say I want to replace my team with ai. Like what? What manager is going to do that? No. What they want is they're always going to want new headcount. They likely can't get it. That's probably a lever they don't have to pull often.

And so, the incentive is to upskill your team as much as possible to utilize the AI tools to be more productive. And it's an increase in your human capital and your knowledge capital. And so that's an incentive in favor of. Not replacing but rather collaborating. Now, there's this other piece out there, which is if I am looking to restructure reallocate resources across my entire company one way in which I can motivate change is by reducing, keeping all the, in all the goals and requirements the same, and then you're forced to innovate.

I don't know how much that is actually going on, but it's, I can't imagine it's not present.

Dave Dougherty: I think for me too, one of the main things is what's your KPI? Because if you're talking about operational efficiency and throughput, that might be a really good talking point for your shareholders if you're publicly traded, if you are, privately held or. You're running your own thing. You could look at revenue per employee.

So, if you see an increase in revenue per employee, then wonderful AI is having a positive effect. But then are they you forcing them to do more? How's your, morale, are they still burnt out? And I'd be curious to find out what organizations are actually paying attention to. And does that vary, depend on the size they're in because, yeah, I think that cult of new finance, as we've talked about before, affects the bigger companies more so than some of the smaller, new ones, newer ones. But Alex, you've been awfully quiet. What are you thinking?

Future of AI and Workforce Dynamics

Dave Dougherty: I don't know, just a couple responses to that.

Alex Pokorny: One of them is interesting from the Amazon one.

Because there's actually a lot there of the old way of thinking is also entrenched, so that's a change management problem. And the older, more senior individuals have basically more gravitas, more credit to their voice. And sometimes they are the literal admins of systems. Stuff like that.

Dave Dougherty: Right.

Alex Pokorny: They are the bottleneck, quite literally. And then there's nothing else you can do about it. So, to have new people with new ideas that don't get dismissed, that would take a change management aspect to come up with new ideas. You can mess with your AI tool as well. There are, I think it's the P value, it's usually at 1.0, but you put on 0.7.

Comes up with wild stuff. So there, there's ways to actually get it to be more creative. It can be annoying because it is a whole bunch of random ideas. So, you have to still work with it. And the newer models are supposed to be like Chad g, BT 5.0 versus 5.1. Supposed to be better with remembering instructions.

So, for instance, I have mine, I tell it to be pragmatic and not have that no emojis. There's a couple of things I've asked to do just stop very

Dave Dougherty: Alex.

Alex Pokorny: I'm looking for a, like a business partner, right? Not a cheerleader, like I need someone to actually tear apart the idea so that it becomes a better idea instead of just telling me how to do it.

Right.

Other part about that is almost on the same, on the target side is it's not smooth yet in terms of being a partner. Like I can come up with a piece of content with it, but then to get that piece of content live is a whole bunch of aspects of our website and CMS and our systems and processes and all the rest of that kind of stuff.

Right.

And it, it doesn't really know the customization that I have of the CMS, so it couldn't really give me a tutorial step by step. It can give me a little. Loom videos or anything else to totalize me through creating the content of publishing the page. No, that's there it's non-custom or it's custom stuff.

It's non-standard stuff or the ticket, process that we have. It's completely custom, so it's not going to be able to help me there either.

I

can screenshot some things and really try to do it, but my gosh it's going to be a time suck. So, some of this I think is like where I AI will be.

But it's not there yet. But it gets down to these other points that I was thinking about was, there, there's a I mentioned on a prior episode, there's a recruiter who mentioned it's not what you know, it's what you're able to learn by next week because it's starting to get to this point of, if you can follow instructions along with chat, but you might be able to work in a system that you have absolutely no idea what you're doing in it. And you might be able to get something done. So, it's not so much about great, you did this in the past once. The systems are new, things have changed. Can you learn this by next week? Can you figure it out enough for next week?

You don't need to know all the Python, but you just got to get this one thing to run. Can you figure it out? Can you troubleshoot it? And that's a, that. Is also this burnout inducing aspect, which I swear everyone is feeling right now, is either, man, we all got really tired of late-stage capitalism all at once,

or

I think the companies are grinding the people to a pace a little bit too hard on this one, along with the job insecurity that kind of gets present as well of having extra people, nearshore or offshore as a cost saving measure is a lot different than an AI tool because I take a sick day, the AI tool is not running right, but my colleague in Costa Rica is probably still working, and then when they're sick, I'm still working. And that actually allows both of us to go on vacation and not be go insane like all of these things.

The Hype Cycle and Current Limitations

Alex Pokorny: I think our, if I'm going to tie it back to hype cycle, are all hype cycle right now. Those are all beliefs of what may occur in the future, but we have not reached any of those yet. We're not smooth enough that I can throw on my ray van meta glasses, and it'll tell me exactly how to do the plumbing project that I have and scan the room or whatever.

It's not there yet. It can give me a YouTube tutorial. That'd be cool, but that's as best as it gets. I did fix a toilet with Gemini. Yeah, I know you did that one time. I kept asking questions back and forth, but it's working off enough of a standardized system that works. Once you start giving it a custom situation, then it's going to start to fall apart, which sadly, I think we've all realized the spaghetti system that is work.

It is a mess. It somehow gets done. It is crazy in the middle. And honestly, if the workarounds are used more often than the standard process that is now the way it works and things just start to shift like a river going through land, like it just, it always keeps moving.

Dave Dougherty: I haven't, I was a downer.

No, it's fine. I had, thank you for taking my sandbox from me for a little while. I'll give you a shovel. You can come back in. I yeah, I will forever occupy the ER space, but,

Self-Awareness in Leadership

Dave Dougherty: You know that well, but that brings me to one point is how many senior leaders do you know that are self-aware enough to realize that they are bringing bias to what they're doing? I, that's asking, there was one

Alex Pokorny: guy once, there was one guy once, one guy. He was amazingly humble and it was an individual who realized and. Spoke about his mistakes, right? And about how things could have been done better and how he should have done things better and about his beliefs and all the rest and how that shapes right.

The way he works. So, he is very self-aware, most individuals,

Dave Dougherty: right? That guy was probably fired two weeks after that speech, right? He did change jobs. Yeah,

Alex Pokorny: it was a better job.

Yeah. Yeah. That, that, that's a difficult call and

I think it's also; it's an aspect of how management is done and leadership is done today. Yeah. We are so far away from our customers and we're so far away from the work. Yeah. That it becomes very hard to have people have a realistic idea of what's needed to actually get the work done and what the customers actually want.

Simon Sinek had that quote from a talk that he gave where he keeps hearing over and over again from CEOs of oh, the customer's voice is what matters to us. And he's bs, you haven't talked to a customer in seven years, maybe 10. What do you know? And frankly, they don't. They know the system and their financials for

Dave Dougherty: Yeah, because they're talking to shareholders.

Those are their customers. The people who actually buy the products are not. The CEO's customers. But again, to Ruthie's point, it's incentives. Here we are. Ruthie, any thoughts on this?

Ruthi Corcoran: You guys are being super gloomy. Yeah.

Alex Pokorny: Okay.

AI Tools and Integration Challenges

Alex Pokorny: Hey, I think AI can get there. I think it will get there.

I just don't think it's there yet. And I think it's unfair to treat your people with the assumption of where it's going to be. I don't think it's. I think that's real.

Ruthi Corcoran: Yes. You got to buy the product as it is not, as the next two releases promise it to be a hundred percent agree with that. And some of the things I was thinking about is I think that execution piece that you talked about with being able to execute on the CMS, being able to execute within the ticketing system, I think it exists.

But it, it exists in a, it exists in its own set of tools, which is a layer on top of like an application layer on top of chat, GPT on top of Perplexity, Gemini, et cetera. It doesn't exist natively out of the box. And so, if you're looking at copilot or whatever tool, whatever general LLM, you've given your workforce and you've said you should be able to do these magical things, you're right.

It's not there. And what's missing is you have the application layer. Via a third-party tool, like where they put some special prompting in place, and they maybe have a couple of agents string together to be able to execute on it, and then they give an interface for people to interact with.

Sure. And then you connect that via API integration to whatever your CMS or you. PIM, or et cetera, et cetera. Like those connections, like they do exist, but they're not everywhere yet. And our tools aren't all integrated in talking to each other. And especially if you're working at an enterprise, you can't just take oh, cool chat. GBT just partnered with Figma and now you can create Figma directly from chat GBT. That's not in an enterprise scale, right? So like a, some of those sort of new exciting integrations that exist out there can't be taken advantage of if you're enterprise with particular licenses.

So that's where my head is there. But I think you're right. It's going there. It's just maybe not there today.

The Complexity of Implementing New Tools

Ruthi Corcoran: And I liked your comment about the spaghetti work, which is. Which is where, what some of the lessons I've learned in terms of the new tool hype. Maybe the tool does do the thing in the demo.

Maybe it's awesome, but then you. You have to try and figure out how your spaghetti work layers on top of it. Either you need to train the tool with this weird set of processes that you've created or you have to do the change management of changing your processes in order to make it work with a given tool.

And so, I think that's where the delta in our expectations lies.

Alex Pokorny: Yeah, there's a, I keep in the back of my head thinking of this. There's an XKCD chart of, number of times you do something in a week or a month, and the amount of time you take to do it, and the amount of time you should take to optimize that thing.

And every once in a while, I start hitting that same, I don't know, there's probably a better phrase for it. That probably is a real phrase for it, of the efficiency, like. Delta or something where you hit this point of is it worth trying to explain to this tool how to do this thing? Or is it worth me just doing it myself?

And there's always like this like one, a false expectation of how fast I can do it. Cause that's not true. And also, an expectation of how long it's going to take to figure it out, which honestly, it's kind of hit and miss sometimes. It's a little hard to gauge. So, I don't know. Do you guys feel that same thing when you're using like.

You're re prompting, and you're like, is this worth my effort to reprompt this, or is it, should I just edit it myself? Or something like that?

Ruthi Corcoran: I know the phenomena of how much time should I take to work with an AI to do it? Which is the same of like, how much time should I take to teach a junior level employee to do this thing?

Sure.

Alex Pokorny: And

Ruthi Corcoran: the thing that is hard to keep in mind in those moments, because you just got to get onto the next thing is the potential for scale. Or the now there's somebody else who can do the thing, the growth. Like AI being able to do it has that added thing of you can then potentially scale it by adding on additional tasks.

And so, I'm trying to force myself out of that trap to be like, no, you should create an agent for that. Don't reprompt it. Or if I find myself doing the same thing over and over, figure it out. But it's so hard because it is time consuming and you got to; you're trying to get stuff done.

Alex Pokorny: Yeah. I also have a belief that AI will patch a lot of the API connections. So, there's been always a lot of hybrid websites out there that they're cool because they connect different data together and they just present it together instead of being separate. And there's whole businesses that are based on that concept, but everything in that is all standardized.

So, one system has a standard set of outputs, the other system has to take that in. Do something with it.

And that usually takes a lot of effort and programming, but there's a lot of AI tools out there that with a prompt, and you're right with a chain. Of one to the next. It can basically patch that connection and take that data, transform it the way you need it, turn it into whatever, pull the insights out of it.

So, you display the percentage versus the raw data, whatever it might be.

Yeah,

there's, there is a lot of abilities there and there are some pretty big companies out there, rapidly growing companies that basically are AI wrappers, but they are just well prompted agents in a row and. What they output is pretty decent because they've spent a lot of time at it.

Ruthi Corcoran: It's that phenomena of a bunch of simple things strung together creates complexity like

Alex Pokorny: Yeah.

Ruthi Corcoran: That it's that and that doesn't happen overnight.

Alex Pokorny: And the question of the time, cost and time savings of is it the time cost worth it for me to recreate this entire thing? Or is it time savings if I just buy their version of it?

Dave Dougherty: Two stories, I think to drive home the point if it makes sense outside of my brain. One, whenever I go to a donut shop, I buy a particular donut. And it is an a very simple one because if you can't do the simple things correctly, I can't trust you with cereal and bacon maple, and crazy stuffing, like avocado toast, jelly rolls, like whatever.

And it's just a simple old fashioned glazed donut. It's not a cake one, it's not risen. Just old-fashioned sour cream, donut. And honestly, that has proven really. Really good as the bar for where to actually buy donuts because then you get rid of all the hype, you get rid of whatever, crutches that Cinnamon Toast Crunch and Lucky Charms bring to, the donut party.

And it goes down purely to craft who has taken the time to learn to do the craft of donut making. So that's. Story number one also. They're just so good. Secondly,

Alex Pokorny: shout out to donuts.

Dave Dougherty: Shout out to old fashioned donuts, man. I might have to buy some. Thing number two, you talk about like the zeitgeist and is everybody just done with late sage capitalism?

I find that over lunch. Many of my coworkers are picking interesting topics surrounding a similar thing, and that is the burnout of metrics. So, the other day we were sitting down at lunch, and we were talking about watches because somebody's watch died and they were deciding, do I do the smart watch?

Do I go back to the old one? I don't want to spend more than like $30 on it, but really, honestly, I'll probably have to do, more than that. Whereas for me, it's like I wouldn't even consider a $30 watch because, we prioritize different things, but it came down to the people who wanted all of the health tracking and all of the like, sleep notifications, and they wanted all of the beeps and bops and, whatever.

And then you had people who were like, you know what, I've tried to optimize everything and I hated it. So, I got rid of it and I went back to the old analog stuff and I'm living blissfully, ignorantly of what my blood pressure is the minute I see your face. Yeah. Yeah, I did that. And yeah, I think especially as marketers we're forced to, look at this dashboard and create this one and, how do you prove that your existence should be.

Allowed, or how do your de-risk every decision that we make before I give you any money to try this idea? Like it's just, it's gotten to a point where it's just no, let's just do some stuff and see what works. Like I shouldn't have to hold your hand through everything. You're an adult, you're supposedly good at what you do.

How about you take a risk, anyway, before I start ranting, please jump in with your thoughts.

Balancing Speed and Thoroughness in Decision Making

Alex Pokorny: just one, I've been working on like a homepage redesign. And concepting prototyping, just, I got some ideas on it. There's an interesting aspect to that. In a company of one, I can get AI tools.

Yeah. They really struggle with redesigns. They're great at initial designs. Redesigns not great at, they could pull colors to get that in involved, but man, they struggle. If they start from scratch, just, it seems like just about any tool can create something.

Right,

but that's a, that's almost an aside really.

It's a question of if I were to be super simple and I was a couple of clicks and I could take this, code that I got from chat GBT and bam, it's now live on the company website, that's not necessarily a good thing for the company sake. Because other perspectives should review it. Other individuals should be a part of that decision.

The homepage should not change on a random whim of mine.

Dave Dougherty: Right.

Alex Pokorny: But it'd be really nice if it could, but it's good that it can't, I don't know. I'm, it's a funny like thing of, there's a certain size of company that. Things change. It's not the entrepreneurial man this afternoon, I'm going to go, change the homepage or my logo and you can, but for a large corporation, you shouldn't,

right?

But at the same time, you should get things done quickly. But you should still take some time and be smart. I don't know, like there we're always going to hit this in between aspect of how deep you dive into the details before you go live. And for some people, yeah, it's analysis paralysis and it never stops.

And my gosh, there is a too much point. But there probably is a too much point on the quick launch too.

Ruthi Corcoran: I want to take us back to another thing Alex said. because Alex had a lot to unpack in his, and we've covered most of it. Nothing, thank

Alex Pokorny: you.

The Importance of Learning Agility

Ruthi Corcoran: But we didn't cover this one piece, which, this quote, I don't remember where you said it came from, but it's not what you know, it's what you're able to learn by next week.

And I wanted to highlight that because it also ties back into Dave, some of the things you'd been hearing from some of the college students and such. I can't emphasize enough how important that skill is. And also, it's tricky to look for Yeah. But holy smokes like that to me is a huge thing I look for in in working with new colleagues, in, in potentially hiring is. Are you able to learn a thing so that we can quickly get on the same page and start problem solving together? Yeah. That's ultimately what it’s about it's your ability to learn so that we can move forward. And I think that's huge. And so, the more that you can cultivate that within yourself, and sometimes it's just by just trying free trials.

Of a whole bunch of different tools. Go do a free trial, make a website, see what that's all about. See what the mechanics are, go play around with how you might organize a lot of different images. Go play around with different AI services like. Cultivating that skill is so crucial, and this is one of the things I so value in working with you guys, is we all have developed that, maybe not intentionally, but we haven't.

And so, then when we're like, oh, did you see this thing? And then we all dive in and we go, oh, let's check this thing out. What can it do? And that allows us to then have really cool conversations.

Dave Dougherty: Yeah, I think about the, that quote makes me think of when I was on the agency side and it was being thrown in the deep end oh, hey we promised this client that we could do tag manager implementations.

Do you know how to do it? No. All right. Your job is to figure it out and what It's like, then put this code here and you have to code at that. And I'm like, I don't code. What, my brain, I remember going to some of the web dev guys and just being like, please help me. My brain does not work this way.

It was really good, like I finally got there. But it was like a tear down of your brain and being okay with being uncomfortable with not knowing and knowing when to ask for help and putting yourself in awkward situations, right?

Dave Dougherty: Thank you for paying attention this far. We've hit a number of recent milestones that are pretty cool for us.

So, thank you everybody who's been along this journey with us and leaving reviews and getting in contact with us and saying hi and all of those things. Glad to have you along. Thanks for listening. Like share, subscribe, go get the Pathways newsletter. We're doing some dive.