Naming in an AI Age

AI is your Coach, not your Substitute

• The NameStormers • Season 3 • Episode 4

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In this episode, Mike Carr stresses using AI as a coach, not a replacement—like how real coaches build skills through practice. Citing MIT economist Sinan Aral’s idea of AI as "a bicycle for the mind," Carr shares research showing that AI helped low-performing call center workers improve, with lasting effects even after AI was removed. He applies this to naming, arguing AI should guide users to observe real reactions, not just guess. Success in naming comes from response, not opinion. Used this way, AI sharpens instincts, boosts creativity, and enhances decision-making.

Mike Carr (00:03):

Hi, this is Mike Carr, and this week I want to talk about how to best use ai. And it was prompted by an article in yesterday's Wall Street Journal, April 25th. And the point, and this is really interesting, at least to me, is think about using AI as your coach, not as your substitute. So let's think about what coaches do, right? Coaches do a lot of research. They'll spec out the other team. What are the team's weaknesses? Where can you really take advantage of those weaknesses? They might research new techniques. What's the better way to throw the ball or swing the golf club or use the tennis bracket? They might research the best practices when it comes to conditioning. So they're doing a lot of research behind the scenes. I think that's how most of us use ai. But coaches do some other things too, right? They'll train you, they'll sit down with you one-on-one, and they'll watch your technique and they'll make suggestions, and they might even assign certain exercises to improve your conditioning or your arm strength, or your speed or your agility, whatever.

(01:01):

And then they'll make you practice over and over and over again. And so you're getting better slowly because you're working with a team. You're practicing the same kick, the same throw, same swing, a hundred times, a thousand times, 10,000 times. And finally, what you will start to notice is your coach helps you improve your own skills through that training, through the research they've done, through the practice they make you go through. But there's some interesting things about what a coach does not do. So think about how you use AI or how you're thinking about using AI as a manager or a team leader about how you're looking at AI for your crew. Coach doesn't play the game for you. Coach is not on the field. Coach is on the sidelines, but you're the one on the field, right? You're the one with the skills that AI has helped you hone to win the game, to win the victory, to move the ball down the field, whatever that might be.

(01:53):

And the coach isn't making those split second decisions like you are. When marketplace changes, when branding strategy changes, when a competitor launches a new brand, you need to respond quickly. You're making split second decisions based upon intuition, experience, knowledge, gut observation, data. Who knows, right? So this article was in the Wall Street Journal was titled AI Might Not Take Our Jobs. Interesting, right? AI might not take our jobs, but only if we act quickly. And it was written by Sunhill ion. He's an economist at MIT. And in the article, he start talks about Steve Jobs back in the early seventies and jobs made the statement that if you put a man on a bicycle pound for pound, he is more efficient at traveling than any other animal on the planet. Put man on a bicycle. That combination, pound per pound, most efficient way to travel to take a journey compared to any other animal on the planet.

(02:55):

Sunhill said, let's talk about instead of putting man on a bicycle, let's think about AI as a bicycle for the mind. And he started talking about not automation, which is what everyone's talking about when it comes to ai, right? We're going to automate these tasks. He was talking about augmentation. And boy, the difference is subtle as it is, is huge. And here's an example. One of his students went out and did some research. We looked to call centers. We all know about call centers, right? The obvious easy target that everyone talked about day one when it came to AI is that put AI in a call center and all the poor folks manning the call centers let go, or they're not nearly as many of'em anymore because you don't need as many of 'em. Sure enough, in the research they did, if you gave call center folks an AI bot, they would do better.

(03:38):

The worst folks, the folks that had the least skills or the most area for improvement, they would improve the most, the lowest performers would improve the most with that AI bot. Now, after a few days, if you took that AI bot away, well everyone back to the way that used to work, saw that performance gain disappeared. But here's what's interesting. This is really interesting. After a couple of months, if you took the AI bot away, guess what did not happen? Performance didn't go down. Performance stayed exactly where it was as if they were using the AI bot after just a few months. What it was happening was folks were learning, perhaps unconsciously, perhaps not knowingly, but they were learning from the AI bot how to better answer questions. They were being taught, they were being coached on how to be a more effective customer service representative.

(04:31):

So the ramifications of this when it comes to naming, are a big deal and they're a big deal, not just in naming, but in a lot of spaces. But let's talk about naming since that's what this podcast is all about. So one insight that I think is important here when it comes to naming is AI can help us solve the biggest problem, the biggest challenge in naming, I've been doing this for 40 years, almost since 1985, and this challenge was the biggest problem in 19 85, 19 86, and it's the biggest problem in 2025 for 40 years. What do you think it's, is it, what should the name do? What should the name convey? Who should the name appeal to? How does the name fit our brand strategy? You know what? The biggest question is, the biggest problem, the biggest challenge. It's guessing. Stop guessing. This is foundational to so many of the challenges and issues and questions and angst and friction that folks go through when they try to come up with a name and there's no reason for it.

(05:33):

But you have to understand certain principles. So how do you stop guessing? Well, one of the things you have to realize, and this is going to maybe rub you the wrong way, is what you think of your new name doesn't matter. Not at all unless you plan on selling only to yourself, unless you are the target and you're the only target, what you think of the name really doesn't count. It's what your customers think of the name, what your employees think of the name, if they're part of the target. Now let's talk about those customers, okay? Is what they think about the name? Is that important? No, no, no, no, no, no, no. You're saying, oh my gosh, how can that be? Certainly what the customers think of the name is important. No, it's not because they don't think about the name. They react to the name.

(06:17):

That's what's important, not what they think. Because as soon as you ask the question, what do you think of the name, you're getting a biased response because in 99% of the cases that we've researched, and we've been doing this for a long time, people don't consciously think about a name. They simply react to a name. And we have ways to assess that. You can't ask the question. You have to observe behavior. So we have ways to observe behavior and assess which name is going to garner that quick, instant, almost subconscious unconscious reaction, which means you've grabbed them right now, you have their attention, and now all the other things that you're interested in come into play. But if they don't react to the name, you can't grab them. Nothing else matters. All this other stuff that everybody's so focused on is irrelevant, and it's so maddening.

(07:04):

And AI doesn't necessarily recognize that. But if you prompt AI the right way, it can certainly help you, right? So part of this is using AI as that bicycle of the mind. You're bringing certain knowledge and expertise to the table, and you recognize that, yeah, AI can be an invaluable research partner, but not so much to automate things necessarily. It's to augment what you can already do. And if you guide it the right way and it comes back and coaches you the right way, you can stop guessing. You can stop worrying about, well, I don't think this name is any good. Doesn't make any difference what you think, right? Well, I don't know if the target likes the name, don't care. But if they react to the name, big deal, right? Well, what are some other things that AI can help you with? And I think another perception is it's out there in the marketplace today, is that, well, AI can come up with great names if you want to name it sounds like all the other great names that are already out there.

(07:53):

And that's the fundamental challenge with the way folks are using AI today is it's all based on historical data. There's not much future focus or insight as to what's going to happen down the road or foresight. And so most of the names AI comes up with are names like everybody else's, even good names, but they're like everybody else's. And you only have one shot at really making a disruption in your space. So Boston Beer came to us years ago, and they wanted to name a new hard cider, and they had the name hardcore, which didn't really work for their female target. There were some negatives, as you can imagine, associated with that. And they were trying to come up with something that was disruptive and that garnered that reaction that they were after and the name they ended up going with that we helped 'em with this angry orchard.

(08:45):

Now, most folks would say, well, you never put angry in the name for anything, right? But it spoke to the alcohol that was in the cider because it was a hard cider. It also spoke to the kind of apple that made the best cider, which were these angry, ugly, nobby looking apples, not the real round smooth apples that you see in the grocery store. And so that name was very disruptive, very different than any name in the category at the time. Could AI have come up with that name? Not the way 99.9% of the people prompt AI right now. But if you understood and you prompted AI the right way and fed it, insights and data about things that sounded different for the category, but had a great story behind them, I think you could use AI to come up with names like that. Another name, liquid Death.

(09:33):

Death also is very strong negative, but such a disruptive name for the category, not the kind of name of anything else like it out there at the time. Liquid Death for a Water would just be such a disconnect when you had Crystal this and blew that and pure this, but very, very disruptive. And so this leads me back to another guessing question or challenge that we run into with a lot of our customers is they'll say, oh, I'll know it when I see it. I'll know the right name when I see it. I'll know the best name when I see it. No, you won't. You will not. If you think that's going to happen, you're going to be sorely disappointed because the best names are often not obvious. They're often a bit uncomfortable like an angry Orchard or a liquid death. They often require context and story to be wrapped around them, which is where AI can really help with that story in that context.

(10:31):

And it can build out something that provides the window dressing you need for that name to come alive for you and for the rest of your team. AI as an augmenter, ai as a coach, and as a teacher, it helps you become a better creative. It helps you do some things quicker when it comes to research, certainly than you could do yourself, but not to automate things totally. When it comes to creativity, when it comes to understanding the research that has been done and the insights, that's where I think the direction that we have seen AI help us the most and where we would suggest you consider using ai. And so I'll leave you with this thought and we'll talk more about this in the next episode or two. There is one thing to focus on when you use AI to create a name or when you try to come up with a name and it's not obvious, and it's not necessarily along the lines of what I've already discussed. So next episode, I'll get into that in a much bigger way. But I hope I left you with some things at least to think about. And I want to thank Send Hill for that marvelous article in the Wall Street Journal earlier this week. Talk to you next time. Bye.