On this week's episode of Inside Outside Innovation, we talk about the addictive nature of AI, the levels of innovation metrics, and how peer influence can make or break your AI rollout. Let's get started.

Inside Outside Innovation is the podcast to help innovation leaders navigate what's next. Each week, we'll give you a front row seat into what it takes to grow and thrive in a world of hyper-uncertainty and accelerating change. Join me, Brian Ardinger, and Miles Zero’s Robyn Bolton as we discuss the latest tools, tactics, and trends for creating innovations with impact. Let's get started.

Podcast Transcript with Brian Ardinger and Robyn Bolton

AI Addiction, Innovation Metrics, and Peer Influence in AI Rollouts

[00:00:30] Brian Ardinger: Welcome to another episode of Inside Outside Innovation. I'm your host, Brian Ardinger, and with me, I have Robyn Bolton from Mile Zero. Robyn, welcome. 

[00:00:47] Robyn Bolton: Thank you very much. Great to be here as always. 

[00:00:50] Brian Ardinger: It's another amazing week in innovation and we thought we'd get right to it. The first article we want to talk about is called Acceleration Flow by Raymond Mark from the publication Mold and Yeast.

Why AI Feels Addictive to Builders and Coders

Fascinating article. The basic premise of the article talks about how Raymond is an addict. Not a metaphorical addict, but he is now addicted to building using AI such the fact that he's spending tokens like it's the end of the world. And he talks about this environment where the AI now has created almost a gambling type of a feeling where you vibe code your way to something. You put your tokens in and you pull a slot machine and out comes some type of output that's just good enough to get you to put the next tokens in to try the next prompt and the next prompt.

So, it was a fascinating look behind the scenes that I think just now more and more people are beginning to discover this particular anomaly or environment that people are becoming to find when they start doing this AI stuff. 

[00:01:53] Robyn Bolton: Yeah, this was really interesting. I mean, I use AI every day and I felt this deeply. It actually reminded me of a conversation that I had with someone probably a year and a half, maybe two years ago, who astutely predicted she's like, I think AI is going to become the next cigarettes in terms of being addicting and it's now, it's cheap and plentiful and it's getting us hooked on it.

The Dopamine Loop of Generative AI and Vibe Coding

And then they can raise prices because we're addicted and we'll keep going with it. And this article lays out a really good argument for that. Not using cigarettes but using gaming and gambling as a metaphor and kind of everything that it outlines of, like you said, it's almost right. It's enough to get you to put the next token in. The feeling that you're upleveling and you're gaining capabilities when you're really kind of not. In fact, you're with kind of outsourcing tasks and things, you're actually losing capabilities, but you have the illusion that you're gaining capabilities. It was just really fascinating all of these almost mind tricks that happen when we use AI. 

[00:03:07] Brian Ardinger: I read the article earlier last week and then three people came up to me this week unprompted and said, I'm addicted to this stuff. They just started to, you know, use Claude code or started to get a little bit more deeper than just prompting a chat bot and the word they used was addicted. One, again, it's so easy to get something back out that dopamine hit of, oh, I tried this and actually it's pretty good and let me try if I can go again.

AI FOMO, Always-On Agents, and the Fear of Falling Behind

And then the second addiction is, I'm addicted to the fact that I'm falling behind. I had a coder come up to me and said, I am very worried that I don't want to take a break because what, during my break, I want my agent to be doing something for me.

And so, this constant pressure to interact with the device to continue to move forward is interesting. I think the flip side to that is what are we building and what are we doing? Are we just putting tokens into the machine or are we actually creating value in the process? And I think that's the next phase that people will be hopefully going through. 

[00:04:04] Robyn Bolton: This line struck me, making yourself obsolete feels like freedom, dressed up as ambition. And I just thought, Ooh, that, that hits a little close to home. 

[00:04:14] Brian Ardinger: And well, we will see what happens. I am addicted as well. Probably not to the extent that some of these folks I'm talking to, but, but who knows, you know, there's always next week. 

[00:04:21] Robyn Bolton: Exactly.

How Peer Influence Drives AI Adoption at Work

[00:04:23] Brian Ardinger: The second article I want to talk about today is from HBR. It's talking about Peer influence can make or break your AI rollout.

Fascinating thing about this is HBR took a look at how companies were deploying AI and which ones were being successful at deploying it and which ones were not. And one of the primary findings was the fact that companies and the people that were actually peer reviewing or showing what they were building with their colleagues, and that using peer influence as a way to encourage adoption was actually a more effective way than either a mandate or just giving people the opportunity to interact with these particular tools.

[00:04:59] Robyn Bolton: The degree to which the peer-to-peer learning influences, it was surprising. The fact that it has an influence I didn't find surprising. What I did find really shocking was that leadership communication and leader encouragement had little to no impact on AI usage.

Why Leadership Messaging Alone Does Not Increase AI Usage

And you know, the article does go on to say that like, hey, even though there's no measurable impact, leaders do still play a really important part in encouraging the experimentation. Encouraging the sharing of learnings. I was surprised by that. And also, as I said, not surprised by the peer-to-peer because when somebody you work closely with and trust is like, hey, I'm doing this. It's just kind of this reassurance of like, oh, if I log into the GPT, I'm not going to get fired like, because I've been replaced. Here's someone who's still employed using AI. I can do it. 

[00:05:54] Brian Ardinger: I'm hearing more and more people tell me, oh, I used this tool to do this. Versus in the past, you could see their work and say, you obviously used something for this. But they're more open about sharing those things, and I think that environment in your company to be open to sharing both the good and the bad and like, what's working, what's not working, here's what I'm using it for, I think opens up a lot of doors because I think a lot of people just don't know necessarily how to use this or what particular use cases could be valuable.

It's all about, at this stage, kind of the experimentation and understanding and seeing how other people are experimenting with these tools, I think goes a long way to, again, like they said, adoption and trust in what's being built.

AI Adoption Statistics and the Global Usage Gap

The third article is called 84% of Humans have Never Used AI, and that's either a crisis or an opportunity. And this is from a Medium article and it talks about basically mapping out the fact that 84% of humanity, 6.8 billion people have never interacted with AI. And you know, you and I are constantly bombarded with everybody using AI and hearing about it on a daily basis.

But it's very interesting to think about the impact that it's having and yet the majority of folks have never touched it, never seen it, never seen a use case where it's really going to impact our lives. And what does that inflection point mean given all the money that's being spent, given all the time that's being spent in this, and are we effectively creating a divide of the haves and have nots?

[00:07:18] Robyn Bolton: Setting aside my questions around the measurement of, is it really fair to look at adoption as a percentage of world population? 

[00:07:25] Brian Ardinger: Fair enough.

The Opportunity and Risk Behind Low Global AI Adoption

[00:07:26] Robyn Bolton: It's so, it was interesting because, you know, as you already said, Brian, like we are surrounded every day all the time and messages about AI using AI, experimenting with it. And so, to see that 15 to 25 million people, you know, pay the $20 a month for AI usage. It's a striking stat in terms of how small it is as you consider the greater population.

The argument, I love the article, makes an argument that this is either both, that it is a crisis or an opportunity, and eventually it's like, well, you can't say it's either because we have to look at this in the grand scheme of things of what are we using AI for and how many people need to do that, have that outcome, have that ability. And so, the stats were surprising. 

[00:08:18] Brian Ardinger: Yeah. I mean, I think the other thing is 84% may not have actually downloaded ChatGPT or, or put a prompt into a chat bot. The tangential impact and the tangential things that people are exposed to with a may not even realize they're working or dealing with AI or being exposed to it. And so, I think that's going to be a point to watch as well. 

[00:08:38] Robyn Bolton: No, that's a great point. I actually, watching TV last night, saw a disclaimer of, you know, AI was used to generate this commercial. I was like, yep, there it is. 

[00:08:50] Brian Ardinger: I'm surprised disclaimers won't be on every particular ad. 

[00:08:53] Robyn Bolton: Yes.

Finding Your Niche Through Experimentation and Customer Discovery

[00:08:54] Brian Ardinger: Out there right now. Right. Well, we'll keep going here. The fourth article is F Around and find out from our friend Drew Riley. Drew writes a post talking about the advice that a lot of people give is, you know, hey, find your niche and exploit that niche. You know, start with something that a small group of folks or a small group of customers and kind of go from there.

And the argument in this particular piece talks about that's great and it's a great piece of advice to give, but how do you actually find your niche? It assumes that the niche is kind of sitting there waiting and obvious, but most founders and creators don't necessarily know how to go about finding that particular niche.

And it takes time and effort and effing around and to find out who are those people that actually fall into initial niche or the folks that really want to use your product and stuff. I thought it was a very interesting way to talk about and think about, you know, both the importance of finding those early adopters and the fact that a lot of times you've just got to do stuff to figure out who those early adopters are.

Explore Before You Exploit in Innovation Strategy

[00:09:49] Robyn Bolton: I got to say, Drew, I love you, man. This since I started my own firm, the number one piece of advice that everyone has given me is find a niche, focus on a niche. And I'm always like, no. I acknowledge the validity of that advice. I know that it will make my life easier and I'm still going to reject it because I don't know which niche I want and which one's interesting.

And a line in here that I just loved was the just pick a niche advice is an exploit strategy presented without the explore phase that precedes it? I'm like, yes, you have to have the explore phase. Some people happen into the niche right away and God bless 'em. I'm envious. Most of us need to FAFO. 

[00:10:37] Brian Ardinger: You can even pick a niche that you assume could be the niche, but you're still going to have to go out there and find out if it is, you know, the advice kind of skips that messy part, you know, find a niche. The part where you kind of go wide, you try a bunch of things, you talk to different groups, you find out actually who's using it, what are they using it for, and that particular process is where you go about finding out who's going to pay, who's going to respond, will they come back, and that in, in effect is the niche when you go through that process. But you have to go through that process a lot of times to execute on the advice. 

[00:11:07] Robyn Bolton: Yeah. One of my favorite innovation stories is a group of guys put together an app focused on a niche, bourbon tasting, and that they went and explored, and that app is now known as Instagram, so... 

[00:11:20] Brian Ardinger: exactly.

[00:11:21] Robyn Bolton: You got to go explore.

Levels of Innovation Metrics for Teams, Projects, and Portfolios

[00:11:23] Brian Ardinger: Alright, the last article is from our friend Tristan Cromer. Tristan's actually coming out to the IO2026 summit. He's got an article out called Levels of Innovation Metrics, and what I like about Tristan is, you know, he spends a lot of time in the exploration side of innovation and helping people kind of navigate that, but in a way that's very tactical.

In this particular article, he talks about how do you actually measure innovation. You know, the, the concepts of innovation accounting. And he puts together a pyramid of the things you can think about and how you measure each particular stage. And if you're measuring things at different stages or if you're measuring, you know, what's important to an individual versus what's important to the product itself, you can sometimes get that out of whack. And so, he, he gives a very good visual as well as an opportunity to think through how do you measure innovation in the sense that innovation in itself is very messy and hard to measure.

How to Measure Innovation Without Killing Exploration

[00:12:12] Robyn Bolton: This again, as always, was a really great article from him. And you know, it is funny for me to hear, read his experience where he hears innovation accounting being applied intentionally or unintentionally to individuals and to the team level.

Usually, I just hear about implied at the project, portfolio, and ecosystem level, but. They all do have to work together. And his point that you need to be measuring different things; we need to have a language that separates these things because they all need to be measured. But applying the wrong measure to the wrong thing is going to kill innovation.

It was just, it was a really thoughtful, detailed, practical article that, yeah, think everyone should read because that's kind of the black box of innovation right, is like measuring and are we doing well enough? 

[00:13:03] Brian Ardinger: What I liked about it, it talks about, again, this pyramid where at the base you have the individual and you got to measure, do the people that are doing innovation have the skill sets, the mindsets, the tool sets to get the job done.

And first you have to level up that because without the individual to be able to execute on it, you're not going to get innovation in itself. Then secondly, the second level is the team. You know, how does the team progress and validate particular projects. Third is the project itself. How do you measure the effects of the project? You know, is it actually getting attention? Is it driving value in the marketplace, et cetera.

Why a Failed Project Can Still Mean a Successful Innovation Team

And then the next part of the pyramid is the portfolio. And do you have enough of these projects that can actually create innovation knowing that a lot of them are going to fail? And then finally, the ecosystem itself. Do you have, you know, an environment such that this can repeat itself and create an environment and culture where innovation can actually happen? 

[00:13:51] Robyn Bolton: I mean, you make such a great point. Fairly early in the article around a failing project is not an indicator of a failing team. It's actually an indicator of a really successful team.

And too often I see those things getting conflated within companies of, oh, if we didn't get an idea to market, the team has failed. It's. Not necessarily. If they killed it quickly, they actually succeeded. 

[00:14:14] Brian Ardinger: Again, the reason I think this is an important article is because especially in corporate innovation, there's this push to measure everything because most corporates think about measuring, but I think they oftentimes think about it in the concept of execution versus the concept of exploration. And giving some tools for the corporate to understand that this is the messy part and here are ways you can measure messiness, I think is an important part because I think a lot of people in the corporate environment are saying like, how's this innovation work and, and why can't we measure it?

Corporate Innovation, AI Strategy, and the IO2026 Summit

Great stuff from Tristan. Come out and see him on April 13th, and with that, that concludes another episode of Inside Outside Innovation. We'll come back to you next week with some more exciting stuff. In the meantime, check out IO2026.com. We have a few tickets remaining for our summit that's coming up on April 13th here in Lincoln, Nebraska.

Look forward to seeing you there if you can't make it, we look forward to seeing you on the inside or the outside at insideoutside.io. Thanks for coming out.

That's it for another episode of Inside Outside Innovation. Today's episode was produced and engineered by Susan Stibal. If you want to learn more about our teams, our content, our services, check out insideoutside.io or if you want to connect with Robyn Bolton, go to MileZero.io, and until next time, go out and innovate.

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