Treat AI As A Teammate (Not A Toolstack) | Austin Myers

We need to look at the actual conversion, the signal, the real intent moment, and decide: was it really an intent moment?

One of the things that we spoke about is the majority of AI tools now fail—upwards of 38% of AI tools and AI initiatives fail. You have to onboard AI the same way you onboard your people, and you have to train it and you have to update it and give it the current best practices.

Welcome back to another episode of the Bridge the Gap podcast powered by Revenue Reimagined. Today is a very special episode for a couple of reasons. We'll start with our guest Austin Meyers, who's VP of Sales and Marketing at Sales AI, a go-to-market leader who spent more than 15 years leading sales ops, CS, and go-to-market teams through every kind of high-growth chaos imaginable. He scales teams across healthcare, SaaS, and tech, served as COO and CRO for startups and growth companies, and now helps organizations remove one of their biggest barriers, which is, as we all know, capacity. We're going to talk about what really breaks go-to-market systems, why strategy isn't the culprit, and how AI can finally give sales and ops teams real leverage instead of more noise. That's number one, and Austin Meyers, welcome to the show. But number two is everyone sees a different face here today. We are upleveling. We have pushed baby in the corner. Dale is away. And we have the third co-founder of Revenue Reimagined with us co-hosting the show today—another very special guest, Mr. Jake Renie. Thank you both for spending your Friday afternoon with me.

Thanks for having me.

Let's not get the guest too comfortable with this, Adam. I don't know if they're going to want Dale to come back. So I'll do my best not to give a 100% today.

I mean, I feel like that's what I do every day. No, I'm kidding, I'm kidding.

Well, Austin, hey, welcome to the show, man. We're thrilled to have you. You know, I think to get things kicked off, Adam was talking about the capacity problem. Let's jump into that initially with this thought: you cannot scroll on your social, you can't open up your inbox, you can't sign into LinkedIn without hearing about the discussion of AI and the impact that it's having on sales teams, revenue operations, reducing cost as a whole. And the pressures of AI in organizations is creating the conversation around how can we get more for less. And as companies are trying to figure out how to implement efficiencies with AI, experiment—and let's not get there yet—but even initial failures with those experimentations, the idea and the expectation is we need more, we need greater capacity. So the question I guess I would lean into you first: as companies are trying to figure out how to get more for less and starting to try to scale back while not properly implementing AI strategy, where do you see some of the greatest go-to-market failures stemming from capacity and not necessarily bad strategy as a whole?

Yeah. So capacity, I've seen this time and time again, and I feel like I'm going to be preaching to the choir here, especially with Adam and Jake and Dale. We've had these observations of how go-to-market teams have done things to date, even today in the age of AI. The irony is we talk about capacity as a problem because we keep saying do more with less, but the default behavior we keep seeing over and over and over is to just do more with more. Um, and so the problem that we're seeing in the world of AI—it's just sometimes it's history repeating itself over and over again. Some of the challenges that even we've wrestled with in our own product, but also out there in the market, is people are treating AI the same way that we treated email sequencers and call power dials. We keep looking at the volume game and we overquantify on math. So the capacity problem, as we've looked at it, it's less of a mathematical problem, because the ways of doing things from the early 2000s and even in the '90s and even 2010s and literally last year was: how do we just make the math work at a larger scale rather than looking at the moments that actually convert? It's a discipline problem because we're not going back to the specific moments that are, number one, the just the high-converting moments, and number two, the most important moments that we actually need to scale and optimize for.

I could use parallels like cold email, right? Cold email—we evolved into the sequencers like Apollo, and because we fell into that as a result of trying to scale manual email. And now tools like SmartLead and you know, Sales Loft and Outreach—everything broken. Every—I'm sorry, I have to interrupt. Listen, I think Manny Medina is an incredible human being. I think Outreach had its place. I think Sales Loft with Kyle Porter had its place, but I think that from what they were supposed to be to what they turned into, and now with some of these other platforms, they have destroyed sales. Go send 50,000 emails. Go send a 100,000 emails, and we get a sub-1% response rate. And that's great because you got some people to respond. It's everything that's wrong with selling.

Yes. And that's what's breaking it. And so we've just taken AI and we've just kept scaling it right.

And so the capacity to just kind of tie up the thought—it's the capacity problem we're dealing with because we've never kind of gone through and fixed the core. And you know, Kevin Dorsey, KD, says this all the time: sales killed sales. And that's a big problem. Marketing is killing kind of marketing. And so the problem we're dealing with capacity is usually because we are addicted to scale. We're actually not looking at the highest-converting moments and then saying how do we do more of that and facilitate those moments? It comes from discipline—just not we love our pipeline created, our deals created, but we forget: how do you just build meaningful connection? And you don't just go piss off an entire TAM with a one-size-fits-all or even this highly personalized targeted approach that burns, turns, and weighs on the capacity. And so what we then find is man, conversion rates are down. Um, so let's go and just figure out how to fill that with AI. And right now, what we've even gone through and what we're trying to even kind of teach the world here at Sales AI is you don't need this limitless capacity. What you need is just focus, an obsession around your buyers and your prospects and your audience, and then scale the conversations that matter there. Scale the moments that facilitate a good conversation in a high-converting moment rather than just trying to find it and capture demand. You have to be able to manufacture demand. And that's the capacity problem like we need to be talking about—the moments that matter, not just pure operational hours and minutes in the day.

Right. So what you're saying, Austin, is—and which is funny because it seems counterintuitive to what we come across most, you know, leaders are out there doing—is smashing the more button doesn't work, is what you're saying?

Right. If A plus B equals C, people are thinking well, if I do more of A and I do more of B, then I'll get more of C. Um, but as we try to get—I guess let's hold off on the moments that matter conversation first, because I think that's spot on. But what I want to know is: how can teams identify the warning signs of overloading their teams, right? Sales, CS, ops—they're drowning, right? In the more-with-more problem. What are the warning signs?

Yeah. Um, so I think it comes down to what we'll call the sublevel metrics—things that are under the surface. And I'll give a practical example, um, because this is something that we've really wrestled through recently. Um, MQLs. We need more, right? The classic—this is classic, right? Like everyone had this problem. Jake and I have very strong thoughts about MQLs. We talk about it weekly.

And we love them.

The leads suck, you know.

Yeah, sales leads are terrible.

Yes, marketing. We're just missing more. We just need to work harder. Um, and what we found was it was actually an intent problem. Half of the leads that we're dealing with and calling NQLs—it was a bad measurement. And as we took a step back, half of them, they were just low intent. They were taking an action that we failed to recognize as them actually wanting to talk to us. And when we started digging in the ones that were low quality and they still would book, sometimes they would even go into the pipeline, and we were converting them at a 1% rate. So what was the default behavior? Hit the phones, call them, reach out, text, email. Noise, noise, noise, noise, noise, noise. And we had to just pull back and say: we don't need more. We don't need to work harder. We need to look at the actual conversion, the signal, the real intent moment, and decide: was it really an intent moment? Um, and we found ourselves even asking some of the prospects, hey, when you did this, were you in the mindset? Because we realized even some of the stuff we were measuring was broken. And so I think when...

It comes to it, it can be people that are in pipeline. This can be post-sales pipeline. This can be just top of the funnel. We have to actually tie the right behaviors to intent and signal moments because right now we've kind of taken the playbook of the past and just said a form fill or a download or clicked or visited a page was intent. But that doesn't always translate to intent. And so we have to be willing as go-to-market leaders to let go of the assumptions and the playbooks that we knew. I even struggle with this. I've known these things for the last 15 years. They worked, but they're actually not working anymore. And we have to be able to let go of what we know and get back into founder mode and just ask, talk to customers, to prospects, to buyers. That's what we've been missing in the go-to-market world is getting back to founder mode.

So I agree with you, but I think Jake and I talk about this all the time. There are so many marketing teams and marketing leaders now that are so focused on, "Well, I provided MQLs." And dude, like we're up from 476 to 864 MQLs, and your sales team just might not be able to close them. And I think that narrative is completely backwards. Yeah, somewhere, somehow, someone came up with this concept of an MQL and made a lot of marketers a lot of money because that's how they were measured, that's how they were compensated. Yet us sales folks still have to close deals to get paid. But I digress. We got MQLs. So we're all good. I think as we look at the modern way to build pipeline and the modern way to grow revenue, we're starting to see that shift. And I think the most progressive CEOs, the founder-mode CEOs if you will, are starting to focus on what matters. And I think this is where AI helps a little bit. But most AI tools—and it's funny, Jake is back in Utah, but he was in Miami with us this week. We were leading an AI event for one of our banking partners, and one of the things that we spoke about is the majority of AI tools now fail upwards of 38% of AI tools and AI initiatives fail. One study was as high as 46%. And I think that's because they are built for what we'll call 3 to 5% of users, which are like the power users. So when you're looking at AI and how AI could help with capacity, how do you address this so that we're not using AI tools that only the best of the best could use? Whether that be an N8, whether that be just any of the complicated tools—it's like, "Oh, I'm going to send you this 47-node N8N workflow and you're going to do great." I mean, sure, if you're a coder.

Yes. You know, I think there are a lot of tools in the market and a lot of platforms that are coming out that are going low code, right? I scroll through my feed and I just got fed up seeing everyone was showcasing their 500-million-step workflow. It didn't matter what the workflow was. It was all the rage. All the cool kids were doing it and we all got FOMO.

Comment workflow and I'll give you my secret for free.

Yes. If I saw one more of those, I was ready to just delete my LinkedIn. But I think what we found was we had to take a step back. This is about four months ago, and realizing most people don't actually know how to prepare for AI. So much so that we even developed an AI readiness assessment for people to just figure out—we're an AI company, right, as sales AI—and but we had to be honest with ourselves and say, are customers even ready? And so there are foundational things, elephant in the room. We talk about it a lot, but it's data at the core. I mean, garbage in, garbage out. And AI is really good at coming up with context. It's really bad at giving you the right context. So, an example: if there are holes in your data, it's going to fill the hole, but probably with the wrong thing. And so we're shocked when it's hallucinating all of a sudden, but we're acting like our data wasn't completely broken to begin with. And so we find people need to go back to the core and address the data problem. That's one pillar that is just straightforward.

The second is a process problem. And I say a process problem: most people actually didn't have a defined process for how things should be done. And so the actual system that needs to happen, the sequence of events, people lacked process. And then we went into—you know, there's the MIT study that says the more we interact with ChatGPT, we turn off our brains. Go-to-market did the same thing. So we turn off our brains and say AI can just run it, even though I don't have a process.

And then the third pillar is onboarding. We onboard humans and, you know, the three of us have brought on people in our teams and we obsess, right? We listen to the calls, we read the emails, we coach every day. But then with AI, we just decided we're not going to do that. It should just plug and play and I'll spot-check it a couple times. The three disciplines are data. You've got to have really good, clean data and understand how you manage the data. Number two, you need to have very clearly articulated, defined processes with the rights. And third, you have to onboard AI the same way you onboard your people, and you have to train it and you have to update it and give it the current best practices. And people are forgetting that, you know, it's we need to treat AI as part of your org chart. So how you onboard, how you train, how you upscale—those three things we found have been missing, even in customers that failed on our product and other go-to-market leaders I've talked to that have failed in their implementations. It was usually one of the three or all three. One of those things was missing and they completely failed and botched the implementation because they weren't ready.

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And that's what's failing. The companies that are winning, they were ready. They've prepared. They've been AI-forward. The ones that are falling behind, it's because they never took the proper steps. And those three things are the steps go-to-market teams in every company need to take now to make sure they can incorporate AI and be part of the top 3 to 5% power users.

It's when you talk about AI being part of the org chart. I think this was one of my biggest takeaways from the event we did this week. And I'm glad to hear that we weren't the only people who were talking about it because we did not prep you. You were not at that event. But we spent—God, Jake, what, probably 15, 20 minutes—talking about AI being part of the org chart. And I literally, there are people who are having people on their teams managing what they're calling like AI employees or AI agents. But as an employee, I mean, this is a trend that we're starting to see more and more.

Yep. And Clay Sparkle kind of coined the term "go-to-market engineer," right? They created an entire category. But there are a lot of that kind of idea, right? It's, you know, go-to-market engineer is the fun new thing, and whatever your stance is, right? For anyone listening here, you might love it, you might hate it. It's like pineapple on pizza.

Do you like pineapple on your pizza?

You know, I kind of, it depends on who I'm talking to and how much... ask my in-laws. Yes. If I'm by myself, no.

My heart's broken.

I like the concept though, like of the three pillars, right, Austin?

Jake's like, "Adam, shut up."

No, I mean, look, it depends on the joint. [laughter]

What's going to like, it depends on the pizza place, man. Some can pull it off, most can't. Definitely not a Domino's. Right. Go ahead.

No, no, no, and you're pushing like whatever people want to call that role, right? It's really—I would define the go-to-market engineer role that Clay has kind of created that category. That's really what the SDR role should have evolved into, right? And somehow we've called it both. We found that skill set. You're really well-prepared if you're already in that mode. You have the foundations to start layering in AI because you're already thinking in terms of data, process, training, and then you're validating, and you're kind of treating it like a growth lab. The same thing for organizations. So if you're stuck in the past and you're trying to think about, "Can it replace what this job does?" you're probably already behind the eight-ball.

# Transcript

Layer it in sequentially to all the different jobs that somebody does. That's where the magic is. And that's where companies need to start going to. We think about replacing just the headcount entirely, but we need to think about replacing the specific jobs and actions that occur in the day.

Yeah, you're touching on an interesting point here. Looking at history, and Clay's a perfect example, we often see when new technology comes in or new process comes in, and specifically around technology, oftentimes we start to create the roles around the process. We hire for what we believe is the right process or function, and yet it seems to be quite opposite as we evolve. It just becomes part of what we do. We start to then ingrain that process in the role, and it becomes kind of a flip. But right now with CRM, we saw that, and it's just become overall a portion of the revenue operations function. We saw that with Clay. All of a sudden there were these Clay architects coming out of everywhere. Everyone just felt like, well, we should be hiring a role to solve for the process, instead of just making it ingrained in what the function is for the role that is there today. Now we're seeing this with AI. Nobody really knows where it should be owned, and it just tends to be a misconception. I'm curious from your perspective and what you guys see. What are other common big misconceptions? What's one major common misconception you all see about implementing AI and go to market as a whole across organizations?

The biggest misconception is AI can replace an entire job function. So the term we started to use is the job stack. Right? If you think about full stack developers, right? There's full stack SDRs, full stack AEs, full stack marketers. There are different layers of the tools that they use and the jobs that they perform. So as an SDR, as an example, AI SDRs don't work. You go into the market and you ask, and everyone's like, "No, doesn't work because the whole job cannot be replaced by AI. It's not there yet. Sure, we're on the brink of AGI." No, every time we see it, it feels like it's a bigger colossal failure because we're assuming it can just do all the things that a human is trained and knows to do, reason and do all the tasks. What AI is really good at is when we narrow down to one process, one job, and you can start layering those in. So in a specific example is follow-up no-shows. Great. If you have a defined process, defined way of working, AI can replace that. But it's not going to replace the entire SDR or the entire AE, but it can start taking those little slivers of processes, and eventually you're powering up and leveling up the humans. But that's the biggest misconception: this job is AI. We need to start thinking in terms of breaking down the tasks, the processes and actions that can be replaced by AI when they're defined correctly.

So where—no, I just like you summed it up well. Austin, where are you as a leader? I'm curious because the majority of folks who listen to the show, founders, VP level, C level, where are you using AI personally as a sales and marketing leader? Where are you getting the most bang for your buck with AI? And then I'd love to know, to double click on that, what AI you're using.

Absolutely. I mean, many different tools. In my day-to-day, I've been nerding out over Atlas, the browser. I've automated four different things in my morning routine. The way I think and reason about dashboards and data—I look at and pull data from this thing and start writing. I've automated a full hour of my morning just doing browser actions. It's really cool. TLDR: you can have it take control and do things in the background. So I have one monitor up that it's performing actions there, and I have a different monitor where I'm doing other work while it's living in the background. The second thing that, for me as a leader, I'm going to give a shout-out to Attention. I live and die by Attention. It gives both—and the reason is we've, in the past, the term, well, you know, you can't scale yourself. If I had a dollar for every time a CEO told me that. Well, now we can. I can give my team the very kind of Austin GPT. What's the coaching I would receive on these things? It's open source, and so they know, yeah, here's where I can improve. I don't have to sit down and go through, and then the times that I do coach and go through that, it's even higher value because now we're meeting in context. We know the gaps. We know how to upskill and see if training is translating to better performance.

Another thing I live and die by is definitely ChatGPT. You know, obviously I use it every day, and workflows, and automating even some of the tools that I use every day. So a lot of unstructured data that I can move back and forth.

Exactly, between some of the models. Do you go from GPT to Claude or Grock, or have you stuck to GPT? And I'm curious how you go back and forth and perhaps which model.

Claude can actually do PowerPoint as of this morning, and it actually does it well. I love Claude for slide creation and just natural language. I feel Claude kind of writes better, and it can match my tone a lot better in how I write. ChatGPT I think helps kind of synthesize and reason a lot better with what I've done. Grock I love for research because Grock kind of does its own thing. It doesn't really fully depend on the Google search API. It actually searches more than the first ten things. And so I use Grock heavily for research. And then Gemini—because we use the G Suite—I use Gemini a ton. So I think each tool has its own job to be done. I'll use each one for the biggest power lift, but then the biggest tool is our own with Sales AI. You know, for AI calls and conversations, just contextual conversations with prospects and customers, and facilitating the journey through natural language—those are the biggest ones I use every day as a leader, and I kind of lean into it. It's not just time saved, it's revenue driven. But I free myself up so much more to actually do the work than look for the work. So it helps surface all the stuff to the top, and then I can start at 9 a.m. pushing on things rather than looking through dashboards and searching for the work that I need to start doing. So I shouldn't have to work to find my work.

I like that. You shouldn't have to work to find your work.

Yes, and that's what AI definitely does so well for me. But how about you guys? What am I not using? What are you guys finding the most value in? What's a nugget that you found that you're like, if every founder just knew this thing, it would be a game changer?

I'll give one first just because it's top of mind up on my screen. My favorite tool at the moment is Gamma. I think that when it comes to creating impactful slide presentations, especially with their new updates, I'll say it here because I would say it in the meeting. I'm going to be in California in a week for an executive committee meeting for one of our clients. I literally took all of my notes and a bunch of stuff, put it into Gamma, and created my twenty-five-point presentation. Do I need to tweak it? Yes. But is it 90% there that I would ship it and be comfortable with it? Hell yeah. I think they've done an incredible job of that product for twenty dollars a month.

I love it, and I agree. I love Gamma. For those of us that are not the creative brains, you know, I tried—we use Canva and marketing and all that stuff—and my product marketer was like, "Oh lord, here, let me—you got the body of this right. Just let me make this not look like shit."

J, what else do you love?

The funnest one by far now for creative processing is the combination of Open Art to create characters, and then taking those characters and kind of scenes and using V3. I like V3 a little bit better than Sora to create videos. So we actually will use it for ad creatives and things that are video, not using it to spoof that this is human—this is AI—but it's the fun creative element. And instead of just using stock footage, we can actually go to the level of creativity we want for a certain video.

Like promotion and all that fun stuff, so that has been done. Video at all? Okay, if you haven't, I would 10 out of 10 recommend Open Art for character generation. And you can get character consistency V3 using its Flow, which is what Google gives you to use on top of V03. And you can just roll through. It's the simplest tech stack. There's a little bit of a learning curve, but once you learn it, it unlocks a whole level of creativity.

I used to be big on Midjourney. Sometimes you can do Midjourney. I don't use it quite as much for character, but you can start in Midjourney, create your scene, and then use V0 to actually bring it to life. So that one's been really huge for video and things that look different, especially for LinkedIn content, long-form YouTube. It's just a different way, and it unlocks looking different because, you know, we both know Jared Robin, and there's one thing that the conversations we have every day is like, how do we just get away from the sea of sameness? And instead of using AI to look like everything else and scale that, how do we actually use it to look very different and delightfully different? So that's what we use. Learning on those platforms, it's funny though. In the process of this conversation, you're bringing up a worthy issue or worthy topic. It's expensive, right? Like it adds up. If you're going to be paying whatever $20, $30 a month for one, you're going to be paying the same for Cloud. You're going to be paying for Gamma. Then you're going to be paying. Like, this adds up. And I don't even know if every time we're realizing how much we're paying out of pocket for these playgrounds that we have and all to produce what? Is there actual return on some of this playing that we're doing?

The funny thing is, I think you can actually draw a parallel to what is happening in go-to-market right now. We're seeing there's a lot of excitement with the playground of AI and what people believe about the art of possible, what we can do. So there's a lot of experimentation to then get what, you know, our partner Dale likes to talk about this concept of the productivity paradox. Because we're so involved in these tools and testing and playing, it feels productive because we're busy and we're active. Are we getting somewhere? And I guess what would be one piece of advice for you to give those listeners who are feeling that pain right now, saying like, we've tried a lot of cool ideas, very few have really proven an ROI. What would you give there?

So it comes back to jobs to be done. I've heard a lot and observed in the market is like there are a bunch of edicts and there's all this pressure like we have to be using AI guys, put AI into this process. So in this rush, we're really curious. Amongst all the scrutiny, the way that we buy every other platform, right, we go back and we're like, "Here's the way I buy SaaS. I'm looking for this thing and this thing. I have a criteria and I have a committee and an approval process and an ROI justification." But then with AI, we're just like, "Nah, let's just roll the dice, right?" And so the buyer behavior I've seen with AI platforms is just scoop it up, try it, and then there's no end in sight to measure. Like, why do we? What were we even trying to do with this? And so it just was an attempt to play with it.

So I think, latticing all the way back, we need to be honest with ourselves before we do that because I've even had to wrestle with that here lately. I'm like, "What am I trying to do?" I need to just pause before I hit the pay now button. If I know what I'm trying to do and what I'm trying to solve, I need to just define some sort of metric. Maybe not ROI on the front end, but the metric I need to measure and move. And if I know what metric I'm trying to move with that, then I can layer back and say, "Do I have when it comes to AI readiness, do I have a process? Do I need to think about a process? Do I have? Can I give it the right data depending on the type of tool? And then can I train it and train it on an ongoing basis?" And those three pillars again, that's the discipline we have to have when we're buying AI tooling. Otherwise, it just turns into kind of figuring out as we go. And you know, we can't MacGyver the whole thing and shoot from the hip with every single tool, especially when you're missing those three pillars. You're doomed to fail no matter your creative.

But that's why every initiative is failing, candidly.

That's right.

Well, at least 70% are failing is what we're seeing right out there.

Fair enough. It's high enough.

All right, we are coming up on time, which means we need to shift over to some rapid-fire questions. So Austin, here are the rules. No one follows them, but I'm obliged to give them per contractual terms. I'm just going to give them to you. Ten words or less to each question. Otherwise, every word you go over is a hundred dollars I take out of Jake's distributions when I do them next week.

Oh, I'm doing for sure. All right, okay.

All right. What's one GTM role you think is undervalued right now?

Product marketing.

I like it. And the context?

Proper customer journeys.

I like that too. Perfect. All right, I'm going to skip over the next one because I think we touched on it enough. What's one bad leadership habit you have had to unlearn in your career?

Good one. Don't ask questions with expectations from my team. I'm going to go past ten words here, but to ask questions to unlock creativity. It's a principle I just learned here over the last year in a book. The way I was leading was leading with my expectations on the team and the way I would ask questions, and it stifled creativity. So instead, laying back, ask questions that are open-ended and purposely aimed at unlocking creativity. A specific example is instead of asking why did this fail, shift into what did you learn and how do we grow and do this better next time? It's been a big habit for me.

Yeah, I like it. I like that a lot. What's a system or process every go-to-market org should kill today? Right now. You get off this spot. You're done listening to this podcast on the way in to your office. What do you kill right now?

Blasting a hundred thousand cold emails across the entire TAM.

Amen. There are no wrong answers, but that is the only right answer right now.

Solid. Okay. What's a framework, a book, or a mentor that has shaped your leadership that just like first comes to mind?

Play Bigger.

Play Bigger. I like it. It shapes like no matter where you are, you can go and define an entire category. Like you can create a category, define it, but there's strategy. It doesn't just fall into your lap. That has influenced the way I think across every org.

All right, that was like fifty words. So Jay, I guess that's like five. All right, we're cooking.

All right, last one. Austin, what's one thing go-to-market leaders should stop apologizing for?

Being bold. I like that. We get so afraid of being direct and bold in ourselves.

Yeah.

And injecting our personality, and instead we revert back to just corporate jargon. We all do it, and we're afraid, you know, when we get a little bit of backlash. That's okay. We have to know our ICP and our personas and the way they communicate, and we have to be clear with our personality and speak to them. That's like we are for the ICP. And I think sometimes we apologize to the entire world for trying to be who we are as an org in every go-to-market team, and then we just regress into vanilla. We have to be so we don't need to apologize for being bold and being for our audience. So be for somebody, not for everybody.

I love it. Austin Myers, Sales AI, thanks for joining the show, man.

Thank you guys.