Operators Weigh In: Should We Be Using AI in Ecommerce?

Summary

Brain Driven Brands: Operators Weigh In—Should We Be Using AI in Ecommerce?

Release Date: July 17, 2025

In the thought-provoking episode titled "Operators Weigh In: Should We Be Using AI in Ecommerce?" of Brain Driven Brands, host Sarah Levinger engages in an in-depth conversation with two industry experts: Emmett, a full stack developer at No Commerce, and Lily Grace Clark, Head of Community at Shopify and founder of Grace AI. This episode delves into the multifaceted role of Artificial Intelligence (AI) in the ecommerce landscape, exploring both its transformative potential and the ethical considerations it brings forth.

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1. Introduction to Guests and Their Expertise

Sarah kicks off the episode by introducing her guests, emphasizing their interconnectedness on platforms like Twitter and their deep expertise in AI and ecommerce.

• Emmett: Serves as a full stack developer at No Commerce, handling all aspects of implementation for supported brands. His role is critical—“if this goes down, the whole thing goes down” [00:50].

• Lily Grace Clark: As Head of Community at Shopify and founder of Grace AI, Lily brings a unique perspective by merging community building with advanced AI applications. She introduces Grace AI, “a clone of my brain... trained on the last 15 years of my thinking and my frameworks” [01:35].

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2. The Imperative of Using AI in Business

The conversation begins with a fundamental question posed by Sarah: “Should we even be injecting [AI] into our business?” She seeks insights from both guests on the morality and practicality of AI integration.

• Lily Grace Clark advocates strongly for the use of AI, stating, “when there's a new technology available to us, I find it can feel irresponsible to not interact with it” [04:28]. She emphasizes the responsibility to understand AI's capabilities and limitations to shape a better future.

• Emmett raises concerns about the over-reliance on AI, questioning the essence of work and human connection. He muses, “nobody actually did any work... what did we even do?” [05:05], highlighting potential losses in genuine human collaboration.

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3. AI’s Impact on Workflows and Human Roles

The discussion transitions to how AI alters existing workflows and the very nature of work within organizations.

• Emmett points out the inefficiency that can arise when AI takes over tasks without preserving the human element: “if my AI did it and your AI did it, what did we even... from a human connection point, what did we even do?” [06:04].

• Lily Grace Clark underscores the necessity of maintaining human oversight, noting that while AI can handle tasks, “businesses are cyclical... If sales are soft in a certain period, it just means that another period is going to work harder for you” [27:45]. She stresses the importance of integrating AI as a tool rather than a replacement.

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4. Ethical Concerns: Source of Truth and Information Recycling

A significant portion of the conversation delves into the ethical implications of AI, particularly the concern that AI might eventually recycle its own output, leading to a homogeneous information landscape.

• Sarah raises the issue: “if it's scraping long enough, we are taking the output of AI, generating new content and putting it back on the web. Does that mean that eventually everything is going to be AI generated?” [12:09].

• Lily Grace Clark counters this by emphasizing the role of human input: “the human role and responsibility... is to make sure that there's quality input going into the model so it's not recycling itself” [14:07]. She introduces the concept of using tools like Gumloop or custom MCPs to ensure AI interacts with diverse and original data sources.

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5. Maintaining Human Context and Originality in AI Applications

Lily elaborates on the importance of human context in AI's functionality, ensuring that AI applications remain relevant and beneficial.

• She explains how integrating first-party data and ensuring that AI operates within specific business contexts can prevent the dilution of original thought: “All the data that we have has to be contextualized with human input. It can't be looked at in a vacuum” [17:19].

• Emmett adds that without this context, AI-driven decisions could be detrimental: “if you did everything Chat GPT said you'd be out of business than a, you know, 30 days” [29:48].

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6. Future Tools and Integration Strategies in Ecommerce

The panel discusses emerging tools and strategies that can enhance AI's role in ecommerce without undermining human creativity and oversight.

• Lily Grace Clark advocates for the widespread adoption of MCPs (Machine Conversation Protocols), stating, “every single person will be using and talking about MCPs” [31:10]. She believes MCPs will act as translators between different data sources, enabling more seamless integration and communication.

• Emmett highlights practical applications, such as using AI to track trends quickly, enabling brands to “jump on that trend and capitalize on it” [18:19].

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7. Personal Experiences and Use Cases

The guests share their personal experiences with AI tools, providing concrete examples of how AI is currently being utilized in their respective roles.

• Emmett discusses using GitHub Copilot, which aids in generating boilerplate code without completely replacing his role: “it keeps the human part of my brain working” [18:37].

• Lily Grace Clark shares her journey of building Grace AI, reflecting on the challenges and insights gained from cloning her thought processes into an AI model. She emphasizes that “what's more ownable and specific to me is the experience of the way I talk and the way I present my information” [24:04].

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8. The Human Element: Emotion and Creativity

A recurring theme is the irreplaceable human element that AI currently cannot replicate, especially concerning emotion and creativity.

• Emmett poignantly compares AI interactions to experiences lacking human emotion: “imagine going to a Taylor Swift concert, but it's just like, it's just the words... you lose all of the emotion and fun of that experience” [31:34].

• Lily Grace Clark echoes this sentiment, asserting that “what people can't do is clone the human feel” [26:59], highlighting that while AI can mimic actions, it cannot emulate genuine human emotions and connections.

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9. Conclusion: Embracing AI with Caution and Creativity

As the episode draws to a close, the guests acknowledge the rapid evolution of AI and its profound implications on ecommerce and beyond.

• Lily Grace Clark encourages listeners to “make your collaborators' lives easier” by integrating AI thoughtfully and using tools that enhance rather than replace human capabilities.

• Emmett reinforces the idea that AI should serve as a tool to improve teams, not detract from their value: “It's a tool to help improve your team, not detract it” [12:15].

Sarah wraps up the conversation by appreciating the insights shared and highlighting the importance of ongoing dialogue as AI continues to shape the future of ecommerce.

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Notable Quotes

• Emmett on AI and Work: “nobody actually did any work. In the general meaning of what work is now, right? Is like, yeah, I wrote the emails, I wrote this document for you to read and critique and edit. But if my AI did it and your AI did it, what did we even... from a human connection point, what did we even do?” [05:05]

• Lily Grace Clark on AI Responsibility: “when there's a new technology available to us, I find it can feel irresponsible to not interact with it because it's our responsibility to understand it, what it can and can't do” [04:28]

• Emmett on AI Tools: “I only, I'm only using GitHub Copilot to generate some boilerplate code... it keeps the human part of my brain working” [18:37]

• Lily Grace Clark on Cloning Human Thought: “what's more ownable and specific to me is the experience of the way I talk and the way I present my information” [24:04]

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Connect with the Guests

• Emmett: Reach him at @naoton.com or visit his blog.
• Lily Grace Clark: Find her on Twitter @GraceClark.

Stay tuned to Brain Driven Brands as Sarah Levinger continues to explore the intersection of psychology, neuromarketing, and ecommerce to equip brands with the tools to thrive in an ever-evolving digital landscape.

Transcript

[Sarah Lovinger]

Oh, I'm super excited. All right. I'm always supposed to do an intro, but I'm bad at these, so. Welcome to Brain Driven Brands. I'm Sarah Lovinger. I teach people how to use psychology in their brands, specifically for DTC and E commerce. And today I have a really interesting guest off and I'm so excited to talk to both of you. Mostly because I feel like we all follow each other on Twitter and we all chat on Twitter, but we never take these conversations off and get real deep into the weeds. And I love the weeds. They're my favorite place to go. So today I have my lovely friend Emmett, which, Emmett, correct me if I'm wrong, you are a full stack developer at no Commerce, but I think there's something I'm missing. Is that correct?

[Emmett]

Is that your title? It's how many hats do we have these days?

[Sarah Lovinger]

So true.

[Emmett]

But yeah, yeah, developer at no Commerce, but also, you know, all things implementation for the brands that we support.

[Sarah Lovinger]

So you've got like one of those critical jobs and it's like if this goes down, the whole thing goes down. So Emmett, I'm so excited to chat, especially with your experience. You're gonna have, you're gonna have a really interesting take on this. And then I have Lily Grace Clark. This is like the first time you and I have chatted like super deep. Last week we chatted for just 30 minutes and I was like, this girl knows some stuff. You are head of community at Shopify.

[Lily Grace Clark]

I am.

[Sarah Lovinger]

That's kind of a big role. That's like, dang, girl.

[Emmett]

That's.

[Sarah Lovinger]

That, that's a good. Yeah, a good, deep role that you have over there. Anything else to add on that other than like, you connect people heavily on Shopify?

[Lily Grace Clark]

I do, yeah. Two things that are helpful for this conversation. One, I am also the founder of Grace AI, which is a clone of my brain. It's a marketing platform that is trained on the last 15 years of my thinking and my frameworks. So I want to get technical about both the application of AI and the ethics of AI as it pertains to us and what it allows us to do and preserve the human condition. And then the second element of my role that is relevant is that I am exceptionally passionate and always have been about supporting entrepreneurs and how they think. There's no shortage of tech technical tools in the world that teach people how to do things. When AI helps product become technically perfect, what is left? And I think the in person experience and human cognition and the magic that exists in our heads becomes More deserving of protection. So I want to talk a little bit about that. That's partly the thesis of my work and my role.

[Sarah Lovinger]

I'm gonna have so much fun with this conversation. I'm just excited to be in the room over here because I'm like, I got two very smart people here. Yes, I love that you have that experience too because you, you've actually built an AI of your brain, which I think is what lots of people are trying to do. Most everybody is trying to build an AI that will take over pieces of the hard part of business, which is usually the thinking part. So I have a question for both. Eminem, Chris, I think I, I'm curious to see what you guys think about like the, the high level morality of AI because I use it heavily in my day to day. What are, should we be using AI is like the, the first question I want to start with. Should we even be injecting it into our business? And I want to hear, oh, I think Grace. And then we'll do Emmett. Because Emmett, I have another question for you. So what do you think? Should we be using it at all?

[Lily Grace Clark]

I have strong thoughts on this. Why we should be using AI. We should be using it for two reasons. One, when there's a new technology available to us, I find it can feel irresponsible to not interact with it because it's our responsibility to understand it, what it can and can't do. Especially if we feel a responsibility for looking after our industry for the next generation. I care a lot about my work right now, but I especially care about the world that I'm going to be building for the people who are going to come after me. So it is inherent and an obligation in me, in us, to understand what the technology is because the earlier we can mess around with it, the more we can understand the nuances which we'll get into here, which is what we should not be using it for. And I have strong thoughts on that too. Yeah, it's our responsibility. We should be using it.

[Sarah Lovinger]

Okay. Oh, I love this answer. Especially because my child just walks in randomly. But this is what I have to stuff to think about all day, every day is. Yeah, injecting it. I'm like, what happens to him after I start using this stuff and injecting it everywhere? And what happens if I take my brain offline and give it to AI? And now I only use AI. Right. What happens to them? And of course like it. It's different, I think for anybody. Even if you don't have kids like what happens to your friendships and your relationships? What happens to your work environment? It'll change basically everything. Which. Good segue for Emmett.

[Emmett]

I. I have strong feelings about.

[Sarah Lovinger]

Okay, everybody's strong feelings.

[Emmett]

I love it. It's. If. If AI starts, say, creating your to do list, right? Hey, these are the three things you need to get done. You're working with Sarah on this project. If I use my AI to do the. Do the to do list to check it off and then I send it to you, who's also using AI to verify or confirm that this work is done and correct. Like, nobody actually did any work. In the. Meaning, in the, in this general meaning of what work is now, right? Is like, yeah, I wrote the emails, I wrote this document for you to read and critique and edit. But if my AI did it and your AI did it, what did we even, like, from a human connection point, what did we even do?

[Sarah Lovinger]

Like, yeah, that's true.

[Emmett]

And. Oh, it just creates more work. It creates more work that we get to check more boxes from a. It's.

[Sarah Lovinger]

Well. And I was, I was reading somewhere that somebody was talking about. I can't remember a study that was like going viral where people were talking about the fact that. I wish I could remember the source of the study, but in this study they were talking about the fact that they. People who were using AI to generate workflows couldn't remember what was in the AI because they would just take it, generate it, assume it was.

[Emmett]

That was the. That was the mit. That was the mit which. Which. Our conversation on Twitter.

[Sarah Lovinger]

I was going to say it was a part of the one that we were talking about. I couldn't remember if it was mit, if it was Harvard, but yes, this exact issue of like, if you can't remember what the actual workflow was, how will you learn from it and then continuously improve on it? Is that a thing? Like, should we not be worried about that?

[Emmett]

Also, the caveat here with this, with the study is if I was told to use AI to write an essay, I wouldn't care. I did the task right.

[Sarah Lovinger]

Yeah.

[Emmett]

So I don't care about the content of what was in it. It's. I did the task to do the thing right.

[Sarah Lovinger]

Yeah.

[Emmett]

It's like, yeah, I checked the box for you. That. That was my job. Not necessarily caring what the output is, but like, I clicked, I checked the box and that's my job is to check bots. And, and then how do you evaluate that when something goes wrong? Right.

[Sarah Lovinger]

That was your job.

[Lily Grace Clark]

Well, technically, we're in this moment, right now, quite literally today, tomorrow, the next year, next years, we are redefining what our work is and what our responsibility is. I wish I had lived through the invention of the assembly line.

[Sarah Lovinger]

I wish I had lived through the.

[Lily Grace Clark]

Invention of the computer because people's work changed and their inputs change. This is why I think it's in. It's incumbent on us to use these tools so that we understand the edge cases and the boundaries. I want to advocate for everybody using AI as a dance partner and understanding what it frees them up to do more of and also hold this nuance that what I'm saying right now I might feel differently about in six months as we learn more. That is so important and missing from so many conversations. It's how we started talking generally about the fear of what I'm calling intellectual attrition and the weakening of our reasoning muscle, that defaulting to opening an AI tool when there's a problem that feels hard and there's a little friction in our heads, I want to lean into that and talk about that and not run away from it. Because as we're managing teams and people, we're also raising questions that might sound in their mind, like, are you asking me to make myself redundant? Are you asking me to not build my strategic thinking muscles? Help me as an employee and a growing leader, understand what it means to use this tool, but also what part of me needs to keep growing. To your point, if it's not output and it's not quota, what is work?

[Sarah Lovinger]

Then what is it? What is work? I don't know the answer to that question, but it now opens up like crap. Tons of other questions that I'm just like, this is really interesting conversation because it gets down to. I think what Sarah loves to talk about is just like the basis of human emotion, invasive human behavior. Because one of the things that I noticed when AI came out, everybody's really excited to use it, but they. They termed it as like a copy helper, right? It was like a copy tool. We can write all kinds of things in here. And it really only affected or threatened, I would say, one group of people, which was copywriters. Now, the interesting part is it didn't get rid of that role, right? But the very first thing people assumed with this tool was that we can get rid of all of our copywriters now because we don't need them, right? We have an AI tool. 20 bucks a month. I can just get rid of everybody that I pay $80,000 a year for the Second thing that I noticed is as soon as it could do image generation, then we did it again. Everybody just assumed, oh, we can get rid of our designers now. We can get rid of our web designers and our graphic designers. We can get rid of our ad designers. It's interesting that anytime a new AI tool comes in, the very first assumption is, I should get rid of something and directly go over here and use this for everything that I'm trying to build. And I disagree with that mostly because, again, AI doesn't use itself. Like, I mean, we have things like N8N now that you can set up these giant, huge constructions if you really, really want to go that direction. However, even that requires someone to build it. So far, AI cannot build those itself, as far as I know. Emmett, is that right? Have you seen AI building its own N8NFLOWS?

[Emmett]

Okay, no, no, not. Not yet. But it's. I. I mean, the same thing with all the, like, the coding AI tools is like, oh, you can get rid of your engineering team. And it's like, well, but. But what happens when it breaks? Because AI can't. Like, AI can tell you what, like, there's a mistake or there's an error somewhere. Yeah, but you need somebody to go in that actually still has the nuance and the. Why is this section of code here and not working? Yeah, you need somebody to go in and fix that. And it does frustrate me when people are like, oh, well, I can save a couple salaries from using this tool. And it's like, but then, I don't know, it just.

[Sarah Lovinger]

It's.

[Emmett]

It's a tool to help improve your team, not detract it.

[Sarah Lovinger]

Yeah, that's what I'm always surprised is that people default to, like, let's just start removing stuff instead of saying, like, we should use this as an addition. Let's take our copywriter, train our copywriter on this, and then he can. He or she can start doing all this different stuff. And it's an improvement. But it immediately became, we could cut costs. We could cut costs. We could cost. Which I think is more of an infrastructure, like a. Mostly just like a capitalism type of a thing where businesses have to run. Cutting costs is one of the ways that you could get your business to grow. So I want to talk a little bit about the actual workflows, though, and, like, go back into that a second, because we talked about the fact that this is changing workflows, it's changing teams, changing how we feel about ourselves. It's also changing, like, how we actually Run businesses. This is a lot of changes for a tool that really just writes words and copy. So yeah. Okay, so I want to dive deeper into this because I want to know, putting all the like kind of icky aside, there's a lot of benefit to it, but one thing in particular has been like on my mind forever, which is like this is a cyclical resource tool, right? It's like a cyclical, what's the word? It does cycles of things. So the only way the AI can go and do what it does, it has to scrape from somewhere. So it goes and scrapes everywhere, right? Like the Internet and Reddit and all kinds of things. It's got APIs and everything now. So if it's scraping long enough, we are taking the output of AI, generating new content and putting it back on the web. Does that mean that eventually everything is going to be AI generated? And what does that mean for source of truth? Right, like it will information eventually just become indistinguishable? You can't tell whether this is true or not because it's just going to be recycling its own information.

[Lily Grace Clark]

I don't know.

[Sarah Lovinger]

What do you guys think about that? Grace? Yeah. I want to hear your take on this because I'm like, this has been on my head for a while. What happens when it's all the same information?

[Lily Grace Clark]

That's not going to be what happens. It sounds like no, I don't think so. But it's also important for us to define the time horizon we're talking about. And let's just for my answer, thank you. Think about the next two years. I don't have a crystal ball and I don't know how fast technology will leapfrog itself. What I'm talking about is that the human role and responsibility in here is to do two things. One is to make sure that there's quality input going into the model so it's not recycling itself. And I'm speaking vaguely on purpose, but to give us an example, if I'm using AI to help me understand community strategy, what I need to be doing is after I have a meeting, an event, a conversation, learn something. I need to be uploading my human thought into the model that those thoughts come from my head. So I need to make sure that there is something novel and something asymmetric, meaning that data, that first party data, that thinking comes from me. It cannot create yet what we consider original thought, that comes from human experience. And then the second thing we need to do is the tools that we're using give it parameters so you mentioned the way the AI might scrape this infinite canvas that is the Internet. What I'm excited about is using tools like Gumloop or custom mcps to define exactly what is happening. So that instead of treating AI like a vending machine that I go to when I need an idea or when I want a thought partner or when I need something, it is more like an operating system that runs in the background for me to of course automate certain tasks. But more importantly to me, it helps me talk to and connect my data sources so I can speak with them in natural language. So here's an example. An mcp. We know what it is, but if anyone is listening and wants an analogy, I think of it as a translator between two different data sets. So I might have a Google spreadsheet of information on number of orders that have been placed by a Shopify merchant and over here I might have information on how their marketing is performing. Those are typically held in two different places and correlating patterns is very difficult, at least for a marketer. I'm not a data scientist and I'm a non technical person. What my MCP can help me do to some extent is match that information or at least talk to it in a different way just using my regular language. That is an unlock and right now that's something that would be hugely valuable to the way I am thinking about the expansion of community and the true support and up leveling of entrepreneurs. I want to be able to make use of this incredible information that I have and I want to be passively provided insights and reports that I define. That's what I think is more important than creating more AI slop.

[Sarah Lovinger]

I would agree with this 100%, especially because there's so many AI tools that I don't even think people know exist. Like ChatGPT is at the forefront. I think everybody is pretty much well versed with it or at least have heard of it. Everybody else though there, I mean there's even tools that I'm discovering these days that I'm like, I had no idea AI could even do that. And it's. Some of it's terrifying and some of it really is like this is going to cut hours off of my work time because it was things that I.

[Lily Grace Clark]

Use in the most interesting way.

[Sarah Lovinger]

Like, yep, it is, it is part.

[Lily Grace Clark]

Of your actual day to day work. I want to know what Emmett is finding interesting and also thinks is going to change. Like how are you guys using it as a developer? Yeah.

[Sarah Lovinger]

Oh my gosh, I'm so Interested?

[Emmett]

Yeah, that's the. Well, the you bring up. The interesting point is all of this. You have these two sets of data, right. You own them as the brand or the company. I think so much of it comes to the like the speed of being able to track trends.

[Sarah Lovinger]

Yes.

[Emmett]

To for you is you can go, hey, this was popular 12 hours ago. If you can get a video out and increase production quickly, you can jump on that trend and capitalize on it.

[Sarah Lovinger]

Oh my gosh.

[Emmett]

But from like the developer stuff, I only, I'm only using GitHub Copilot in to you know, it generates some boilerplate code. It Using GitHub Copilot has also forced me to one not spend a bunch of money on AI tools because before you know it, you can be spending a thousand dollars a month on tools that you may not be fully using. And yeah, but I think also just like it keeps the human part of my brain working, which as a developer and building building product that is important to other people's businesses. Just becoming sort of a AI drone to just hitting tab on whatever the AI spits out that may not be what the custom our customers want. And I think we're in a really interesting point I think in general is especially in E commerce is so many of these brands have so much data, but they don't know what to do with it.

[Sarah Lovinger]

Thank you. I feel this in my soul. I'm like, but.

[Emmett]

And this is where whoever can come out with the AI tool that figures out, oh, hey, our ad spend is down this month, but our orders are up. Oh, hey, that ad's probably working. Let's double down on it and whatever. Or from what we do is we ask the actual customer. We're not relying on meta or somewhere else to be. Like, your ads came from us. Like, that's. No, you're actually hearing your customers. Hey, where did you hear hear about us? Oh, you know, grandma told me about your T shirts and I bought a pack. You know, that's way more important than having an ad platform be like, oh, they definitely came from us. Give us money. Yeah, yeah, like, keep spending money with us.

[Sarah Lovinger]

Yeah. There's an interesting point too that you just said. The fact that we're just like swimming in data. We're like drowning in it. And this I find one of the most interesting parts about like the business that I have and like the customers that I work with is the fact that half of my job is distilling data where I'm just a data analyst. Like at the end of the day Sarah's job is to go find interesting information, but half of my job is really just to sit down and try and distill what we already have because. And I don't. I'm not sure what to do with this because it's getting worse with AI. It's like, holy crap. Now we have even more ways to try distill the information out. And depending on how you prompt, you're going to get very different information out of it. If you have two different data sets and it's comparing them again, like, it just, ooh. Not that I have, but like founders have is they're constantly coming to me being like, we know everything about our customers. Nothing's working.

[Emmett]

Yes, you may know everything about your customers, but economic conditions play a massive part in if people will buy a product. Especially if it is sort of those like splurge purchases.

[Sarah Lovinger]

Yeah, any sort of. For fun the product usually gets.

[Emmett]

Yeah, I have. I haven't bought many things for fun recently.

[Sarah Lovinger]

Give me both. Okay, I want to shift just slightly because now I have. I have all these questions building in the back of my head and I'm like, oh, I have. I don't know who else to go to answer all these questions. So we talked about, like, Emmett had a really good range of like, here's how we're looking at it from a data standpoint and here's how deep this goes, especially when it comes to like, how businesses should be looking at this. I want to shift back to kind of like how the individual operators should look at this now because all three of us use AI and subcapacity and we're using it for different use cases, different reasons. I want to see, especially Grace, you've built an actual AI that like houses your brain. This is something that I've been super hesitant to do and haven't done yet because I'm just like, the only one I've ever built was basically a data scrape of all my tweets. And that's as far as I've gone because I was just like, I don't know what this means. What was your experience of that? And like, was it helpful? Did you use it a lot? Do people want it? Is it valuable? Like, what was the experience of putting your brain inside a computer? Basically.

[Lily Grace Clark]

So humbling. But I want to say this. There is a chance, a non zero chance, that someone has already built a clone of your brain, because all three of us are fairly 100 outspoken and consistently present on the Internet. And I've been on enough podcasts And I share openly enough that if someone wanted to download transcripts of everything, scrape my tweets, which are publicly available.

[Sarah Lovinger]

Yeah.

[Lily Grace Clark]

And add in their own context, they'd have a large. Largely a working model of how I think.

[Sarah Lovinger]

Yep. So people have told me that they have done that to my Twitter.

[Lily Grace Clark]

I'm not surprised.

[Sarah Lovinger]

You probably shouldn't tell me that. That feels weird, guys.

[Lily Grace Clark]

Yeah. It's a real conversation that we need to be open about. And I think I'll get into the experience of the. The three things that I really learned and think about when I reflect on the process of cloning my brain. But there's a shift from the importance of making your information public so that people. People can find you and people understand what you're about and you're helping people learn and realizing that you are feeding this openly available model where people can create a clone of generally how you share your thoughts on the Internet. And to test that, I've done that with someone else whose name I won't name, but I actually built a working model of their thoughts that are available online. And they helped coach me through an element of my work. But that was just me conversing back and forth with them in a language model that's separate from what I did. I gave specific parameters to build a marketing platform that uses the way that I think. And the two things that I learned were one, humans are so similar, and we operate within three standard deviations of each other. We feel very different. I'm sure all three of us here are thinking I would never. I could. No one would ever confuse me in the way I think for Emmett. But going through the process of training my brain, what I learned by working with my two engineers was that all of the nuance and novelty that I thought existed and all of the specifics I was trying to pack into my model didn't matter. My reasoning is certainly interesting and unique to me, but what's more ownable and specific to me is the experience of the way I talk and the way I present my information. All of my clients who I asked to use Grace AI to help me understand what was similar and different said what's most important to me is that it actually feels like you, which is really me saying brand and experience is so important in the human feel because I could be saying these words and talking really boring and monotone, and the content could be the same, but the feel that we get from each other is so important. So building the brand voice and the specifications of how Grace AI talked and responded was More important to me than the fact that anyone could clone my brain. What people can't do is clone the human feel. And then the second thing that I learned was the importance of connecting that system to my clients systems meaning if my model wasn't operating with the context of their sales from Shopify and their social data from Meta, I was no better than ChatGPT, which is largely operating without the context and the historical data from their business. That is still a very generic language model. So the opportunity is to make it more specific to the problem that you're solving. And that includes exactly what Emmett was saying. The context of the macroeconomic conditions, what is happening culturally, the virality and the trends that you measure. There is human input that has to. All the data that we have has to be contextualized with human input. It can't be looked at in a vacuum. Frankly. Businesses are cyclical. If sales are soft in a certain period, it just means that another period is going to work harder for you or has to work harder.

[Sarah Lovinger]

So true. Yeah. Because humans are cyclical. We go through all kinds of weird phases that just repeat themselves over and over. This in particular, very interesting part, I think. Go ahead, Emmett.

[Emmett]

Well, this, from the data standpoint, this brings up this idea that I heard. It's like having a Y column in your data set of like, why do we have this totally like a, like why are we, why are we storing, you know, marketing metrics? Why are we like why are we holding number of orders this customer has? What? Like, like sort of, sort of these, like having this human context of like this is why this is here. And like you see it in code all the time. Like code comments of like, hey, this function does this weird thing, don't touch it. And like, but it's, it's the same thing. It's like your business is working in the current state. Like you're not going to make giant changes today, right? You're like you're going to slowly make changes based on the context you have of. Or if you put in, hey, fix my business Chat GPT and you, if you did everything Chat GPT said you'd be out of business than a, you know, 30 days, whatever. Like because they don't have the context of context or you know, is business shrinking head count? Right? Is that success, the successful business to you? Most people would say no. Most people don't want to cut staff or their team or. But everything you hear about is like, oh, this can save you, you know, 10 people on your team. It's like Well, I like those 10 people. Why would I want like I like those 10 people.

[Sarah Lovinger]

Yes, thank you, Emmett. I like them and I want them around. That's amazing. I love it.

[Lily Grace Clark]

What I like to do is tell people who are allowed to use AI in their organizations, which is a conversation everyone should be happening. That should be happening is to the extent that you can make your collaborators lives easier. If you have a boss who really likes to get recaps every single Monday, have a tool like Gemini record your calls or granola and then have it analyzed in ChatGPT or Gemini and or Notebook LM turned into a recap that you can easily send to your boss. Make the most important information easy to surface. The second thing that everybody should be doing is to create these automated workflows where your data actually talks to, to itself. And if everybody isn't using and working with MCPS now, that's what will change. By the end of the year, every single person will be using and talking about mcps. And I'm very comfortable with that strong language because I truly believe it. And an MCP is not in itself an AI. It's a translator between. It's a black box where language can talk to it. Different data sources can talk to each other. That's the next thing everyone's going to be seeing and messing around with. That's what I would tell everyone to do who's listening to this is go on Twitter and search MCP and see what tweets come up.

[Sarah Lovinger]

Yeah, I'm sure there's going to be a lot as we go through the next couple years.

[Emmett]

Yeah, it's the like imagine going to a Taylor Swift concert, but it's just like, it's just the words and like, you know, it. You lose all of the emotion and fun of that experience. And it's, it's the same way if you ask.

[Sarah Lovinger]

Yeah.

[Emmett]

Chat to tell you, to tell you like a joke. Right. Like it will tell you the joke and it'll tell you the punchline just fine. But like it's not going to be that, you know. Right.

[Sarah Lovinger]

Yeah, it's so it's missing a lot. Yeah. There's, there's a big human level in AI I think that is missing from, from the, the conversations that we're having. It's a good tool. It's good for workflows, it's good for analyzing things. It's not really all that great for being human. And that's the point.

[Lily Grace Clark]

I'm excited to find the duolingo of brain strength. People are Using AI.

[Sarah Lovinger]

Gosh. One of these days vet it's going to happen.

[Lily Grace Clark]

Emmett can build it in code for us.

[Sarah Lovinger]

Yes. Oh my gosh. Well, and it'll be interesting to see. AI will build its own AI protected. Is that. Yeah. We'll have to see what happens because it's. It. It's morphing so fast that like I'm just here for the ride at this point. I'm like, we're just gonna have to find out what happens. Gonna be really interesting. Couple years. I appreciate you guys coming on. Emmett, where can people find you if they want to like, follow what you're doing? Are you on social media?

[Emmett]

Yeah.

[Sarah Lovinger]

Although the Twitter. Because that's where we talk.

[Emmett]

Twitter. My blog. I have one of those.

[Sarah Lovinger]

I love your blog.

[Emmett]

I'[e-mail] and I think, I think that's the another thing that we will start to see is more people having their own content that's not scrapable. Saying that is starting to roll stuff out to make your site not scrapable. So. Yeah. And you have your own personal site, then who cares what happens to Twitter or LinkedIn or any of the other socials? It's. You have a. You have your own little spot on the Internet.

[Sarah Lovinger]

Yeah. So that you notice things that I don't notice. So I appreciate you. Immense. And then Grace, where could people find you if they want to chat with you? Follow your journey?

[Lily Grace Clark]

Find me on Twitter at Grace Clark. Clark with an e. Yeah. Always there.

[Sarah Lovinger]

All three of us are always on Twitter. That's the conversation and that's the place.

[Lily Grace Clark]

To be, I think.

[Sarah Lovinger]

Okay. This was great. Thank you so much for coming, you guys. I appreciate you. Yeah, this is great.

[Emmett]

Brain Driven Brands is part of the Learn and Laugh series on the Quickfire podcast network and is presented by Tether Insights. For more information go to tetherinsights IO.