Outbound Contact Center
Outbound Contact Center is for sales and marketing leaders at B2C companies who want to make an outsized impact.
Every week we interview practitioners, leaders, and experts to explore how they build their B2C sales organizations – and the tools they use to win over their market.
You’ll stay up to speed on major themes in sales and marketing, learn how to win with a data-first mentality, and get actionable insights you can implement today.
Hosted by a B2C sales specialist, executive and CEO, Alex Levin.
Outbound Contact Center
E6: The future of customer interactions and artificial intelligence with Marc Bernstein of Balto
In this insightful episode, Alex Levin, CEO and Co-Founder of Regal.io, engages in a thought-provoking conversation with Marc Bernstein, CEO of Balto. They dive deep into the world of speech analytics and the transformative impact of real-time guidance in customer interactions. From decoding the nuances of speech patterns to predicting the imminent arrival of Artificial General Intelligence (AGI), this episode offers a comprehensive exploration of the evolving landscape of AI and its implications for businesses.
Key Takeaways:
- Speech Analytics' Transformative Power: Explore how Balto's speech analytics decipher intricate speech patterns, revealing behavioral insights in real-time, providing a game-changing tool for businesses aiming to decode customer interactions effectively.
- Real-Time Guidance Amplifies Insights: Balto's unique approach seamlessly connects insights gained from speech analytics to real-time guidance, empowering organizations to apply learned tactics instantly, scaling them across sales and customer service conversations with a simple push of a button.
- Evolution of AI in Sales and Customer Service: Witness the dynamic shift from sales-centric AI applications to an increased focus on enhancing customer service efficiency, where real-time technology proves invaluable in meeting customer expectations promptly.
- AGI's Imminent Arrival: Brace for the bold prediction as Marc Bernstein anticipates the arrival of Artificial General Intelligence (AGI) within three years, highlighting the potential disruption and the need for individuals to keep pace with rapidly advancing technology.
- Preparing for the Future: With AGI on the horizon, the conversation delves into the significance of staying technologically updated while emphasizing the enduring value of human qualities such as creativity, art, and emotional understanding in the face of AI-driven changes.
To learn more about everything Outbound Contact Center, read more posts at regal.io/blog or email us at hello@regal.io.
Hi this is Alex Levin and I'm here with Mark Bernstein, the founder and CEO of Balto. Thank you for joining us, Mark.
Marc Bernstein:Alex, great to be here.
Alex Levin:So to start you know, maybe tell us a little bit about your journey. How'd you end up being a founder? Was it a traditional path or, you know, did you go off and do lots of different things?
Marc Bernstein:You know, I didn't grow up thinking I'm definitely going to be a founder. It wasn't like I had that North star my entire life, but I found myself always wanting to build stuff and build organizations. I started trading stocks when I was in middle school and I started a stock trading club in high school. And then I had got really into physical fitness. So I became. A personal trainer in my senior year of high school when I started kind of a little personal training business. So, you know, kind of like a nice lifestyle business. And that's where I really learned the fundamentals of starting a business, going out and getting customers, making sure you're taking good care of customers, thinking about your product, which in that case is, you know, the style of training and the results you deliver. And then hiring and expanding the business and making sure managing the numbers. And I grew that lifestyle business to over a hundred clients and a base of five trainers. But I, I noticed that it was almost a little tapped out for potential. You know, it's very difficult to retain. It's a career move. A lot of people kind of go into and then they bounce out of, and then they go back in and they bounce out. So I found myself constantly on this hiring treadmill and thought, you know, if I want to do a business at a bigger scale, I probably should look at an industry like tech. So went to learn you know, and get into the tech space. And, you know, here we are now you know, seven years later with. Starting Balta, which is the, the leading AI company in our space,
Alex Levin:So did you work for other people first in tech or how did you learn about tech in your words?
Marc Bernstein:just a little bit. Right after college I joined another startup here in St. Louis called Top Ops. And it was one of those experiences where you are working with a founder who really knows their stuff. It was Jim Eberlin who had previously started. Gainsight, you know, Unicorn out in the valley. And also even before that, a company called Host Analytics, which ended up becoming Planful. And so this was Jim's third run at starting a tech company. And I was like, you know, this guy probably has a formula. Let me see if I can learn as much from him and the organization, the team as possible. So I was at Top Ops for a year and a half in a sales role. And that's actually where I met one of the other founders, Chris Contes, who pushed me to, to start this with him. I was originally saying, no, I'm happy. I love sales. I love the startup. And, you know, Chris said, you know, Mark, we have to dream big. We have to go do it. You have a good idea. Let's do it. So Top Ops is where I both learned about startups and also got that push to go do the next thing.
Alex Levin:You know, when you think back to those days you know, what were the, what were the best lessons you learned working for other people?
Marc Bernstein:I think. There's, it's so funny cause I keep reprocessing my experience that I had a top ops again and again. And as, when I remember it, I kind of learn new things thinking back on it. So an example is, I remember at the time, you know, there would be some decisions like around personnel or around processes or promotions that I was a brand new, fresh out of college kid. And, you know, I said, I don't understand it. It's crazy. Why are they doing it this way or that way? And now, you know, at this point, nine years later, I'm like, Oh, I kind of get why they made the decisions that they made. So there's almost like this humility that you get. It's a little of like hindsight 2020, but it's almost like humility 2020. When you go back, you're like, Oh, I really didn't know as much as I thought.
Alex Levin:Yeah, I was pushing them to do it my way, but maybe they were, they were right after all.
Marc Bernstein:totally, 100%. And it's like, I just didn't have the perspective to understand their perspective. I didn't have the experience to be able to, like, reverse engineer how they got some decisions that they got to. So that was kind of an interesting lesson. And I also think that's where I really started to learn sales, grinding, and systems. And I think that those three things, sales, grinding, and systems, actually fit super well together. Sales being the craft of having conversations that drive intended outcomes. So you could say, you know, fundraising is a sale. You could say getting folks excited about joining your company is a sale. You could say getting a customer to flipping a customer from being frustrated to being happy is a sale of sorts. But I really learned how to have an effective sales conversation, which is just a purposeful conversation where you're in control thinking thoughtfully about how to progress it rather than just letting it go wherever it goes. And then, you know, the best type of
Alex Levin:know, I know you, you, you see this too, as a founder, you know, you're having having to sort of explain your company every day, five times a day for the rest of your life. So, you know, to your point, you better enjoy it, whether you call it sales or you call it something else, you better enjoy being in that situation or working with people to help them understand the vision you have.
Marc Bernstein:Yeah. Yeah. And if you don't learn from it, when you explain it five times a day, if you just like click into that mechanical mode and say, oh, here's my pitch again, then. You're missing out. Like, I still, I still, every time I explain what my company does. Kind of analyze, how did the person respond? Did it go well or not go well? Does, do I like our messaging as it is now? Do I think it's simple enough? You know, can this resonate with a bunch of different audiences? If I explain this to someone who I was actually at a holiday concert. My wife was singing in a concert this past weekend and there's this very nice couple and they're both in their 80s and they asked what my company did and I was able to explain what our AI did. And they got it and I was like, Ooh, that might be the best explanation I've had that two folks not in tech who are kind of in their eighties were able to pick it up. That was a really cool experience.
Alex Levin:I like it. So let's, so let's go to that. So like, who, who is your typical customer and like, what, what is the pain they're having? What are they turning to you guys to do specifically?
Marc Bernstein:Yep. It tends to be a mid market contact center. In financial services, insurance, or high value retail. Those are kind of our best segments. And the reason is that each of those segments have a good number of representatives on the phone, uh, where they might have, you know, hundreds or thousands of people that are all talking to their customers, you know, for service or retention or new sales or cross sells or. You know, even just solving issues. And if the conversation goes well, then there's a great opportunity. And the conversation doesn't go well, you might lose a very expensive customer. You might have a compliance issue that you just created. You might get your brand blasted on Twitter. So we look for different types of customers where that fork is very important, where you want your representatives at scale to being, having effective conversations with your customers. And we do that with what we call real time guidance. It analyzes everything that customers say live while they say it. Everything the representative says live while the representative says it. And in real time will give that rep recommendations on their computer screen about how they can be as effective as possible. So a customer might call in with a question or they might call in with a complaint. They might say, Hey, there's something wrong with my bill. And our software will hear that and pop up different information and different ways, recommendations to the agent on how they can handle that as effectively as possible.
Alex Levin:Yeah. And so at this point, you guys have, you know, I've gotten pretty big and sort of serve quite a few customers, you know, what's the, what do you think is the secret sauce in this conversation, intelligence and sort of real time sort of analysis world? Because. And the reason I ask is that in B2B, there's a couple like, you know, IPO scale businesses in the space and gong and then, you know, whole course, I guess was an IPO scale, but you know, Apollo, maybe soon. You know, where there's sort of really pretty big businesses in B to C in more sort of consumer businesses. There's nobody at that scale yet. So it still feels like for whatever reason, customers aren't using this technology as much. And so why do you think that is? And like, you know, yeah. What are you seeing sort of that's maturing in this industry?
Marc Bernstein:I think it actually represents a bigger industry shift that's happening right now, and I think that folks in the B2B space like Gong were very smart to capitalize on it early. The shift is that in order to get insights from your conversations, call it 10 years ago. In order to do that, you had to have a technical team run the recordings through a transcription engine, and a technical team then label the transcription in order to pull out various events of, you know, try to just make sense of what happened. And then you would have a data science team. Mine, the different events for insights. They basically would download a giant CSV and look for relationships across all the different events. When a customer has this complaint and the call lasts four minutes, then the chance that they end up happy is 12 percent less. So that used to be a very sophisticated series of processes that needed to get run in order to actually pull out an insight. And what conversation intelligence did, and folks like Gong did a great job of capitalizing on in the B2B space, is they made it easy. They said, you don't need a technical team. You don't need a data science team. You can be just a normal person, you can just be a normal person and you go on into your conversation analytics and you can see insights about what's going well in your conversations and what's not. I think that. The speech analytics really started in the B2C space like speech analytics has actually been around since I think the early 2000s so I think that it's just players in our space are taking more time to catch up. They have technical debt and they have almost momentum debt or culture debt to overcome. To be thinking, how do we cater toward a new type of user? Someone who doesn't have necessarily the same data savvy, but has hypotheses they want to test and things they want to figure out and answers they want to get. So it's a different type of user. And Gong was almost able to start with a fresh slate and in the contact center world folks had to reimagine how they were implementing this technology.
Alex Levin:Yeah. So you brought there was a longer history. And so a little bit more of a mess on the ground that they had to clean up before you could start getting people in sort of more of these. Yeah, I mean, it's, it's in this world, there's a lot of different use cases people talk about. So they talk about sort of a QA use case, coaching use case, an insights use case, a automated next actions use case. You know, what do you think is the killer use case that people, or what should people be thinking about first if they're not doing any of it?
Marc Bernstein:Well, I'll tell you what I hear all the time. The highest level is people say, I want insights. Meaning, I want to learn something about my business that I didn't previously know. And then people usually were, use the word actionable insights. I want it to be something that I can look at and go, Oh, geez, I can go deploy this to my customer service floor, my sales floor and, and make a difference. So then the question is, what kind of insights are there? And you can kind of break it down between. Agent insights and customer insights because there's essentially the two parties on the line, you know customer insights voice of the customer What are they saying? And what are the issues that they have? What do they like? What are they not like, you know? Why are they calling in that is still actually the number one? Most queried question from a speech analytics tool is why are my customers calling people are still trying to figure that out And then on the agent side, what are the agents doing? Well, what are they not doing? Well, what tactics appear to be successful what tactics appear to be problems, you know, what compliance? Mistakes may they have made so those are kind of the different categories of insights You can pull out of it. And I think that the key Is that when an organization learns something from speech analytics, and that that needs to be step one, you have to learn something, you then have to be able to apply it back to your contact center force at scale. If you learn something and can't do anything with it, then what's the point? So our premise actually all along has been at Balto that organizations have what we call an insight backlog. They actually have a lot of things they've learned. And by the way, you don't just have to learn things through speech unless you can learn through books, through blog posts, through best practices, you know, just the stuff that you say, yeah, this is what I think leads to, you know, having a really good business, running a good business. How do you take the things you learn? And then actually scale them out. So it's being reflected in your sales conversations and your customer conversations. So that's something that Balto has really uniquely done is connect those two pieces, the insight where, you know, we help you look in your calls and pick out you know, what is leading to successful outcomes and what's not. And then the real time guidance, you know, allowing you to wish the push of a button scale out whatever the best answer is the right knowledge the right tactic, we like to scale that out to all of your agents with the push of a button. So you're not just learning stuff, but you're applying it
Alex Levin:Yeah, makes sense. And do you think you know, I guess when you go talk with clients today, do you think this is more useful in sort of revenue generating sales use cases? Or do you see more pull in customer service use cases? Or is it kind of even between them?
Marc Bernstein:different uses. And it's changed over time. In the beginning, it was absolutely more useful for sales. And the reason is the ROI is so easy with sales. If you can have an agent. Ask two or three more discovery questions on a call and attempt to overcome an objection where previously they might've just said, okay, thank you, or make an attempt to close the deal and get the sale. Whereas maybe they just would have, you know given up. Well, those are huge levers and you, you actually just win dollars in the bank and it's very easy to pay back the the customer service world. Up until right around after COVID, so call it, you know, or, you know, after the start of COVID call it, you know, mid 2021, right around up until then customer service teams, they put as their North star customer experience at all costs, and that's great that they were trying to really provide great experiences for the customer, but the idea of trying to make your contact center more efficient and reduce the amount of time that it takes to handle a customer. Interaction was a little bit taboo. It was, we don't want to speed up the customer reactions because if we do that, we're going to be rushing them off the phones. But it turns out that now there are a whole bunch of tools, you know, like real time guidance, like real time note taking. That allow you to just pop the right answer up to the agent, the second that they need it, that allow you to automatically take notes for the agent so you can save them time. They don't need to take notes after the call. So those time savings of, you know, 30 seconds here, 30 seconds there really add up and organizations have shifted saying, you know what, we, we've realized that what a customers want most, the thing they want most is not a happy branded greeting. You know, hello, thank you so much for calling. It's my pleasure to serve you today. Like, that's not the thing customers want the most. You know, nor is it this trained empathy where anytime you say something bad, they say, I understand, Alex, that really sounds difficult. Like, that's not the thing that they want the most. What they want the most is for you to answer their questions correctly and quickly. That's what they want the most. So I think organizations have shifted their view to that. So now customer service organizations are adopting this real time technology at an increasing rate. And I think it's about to be almost a 50, 50 split in terms of where the biggest ROI is.
Alex Levin:And you know, we talked about it sort of earlier, you know, we have a couple of customers in common. We focus mostly on outbound and sales teams. So in our case, it's sort of sales organizations. So that going into the specific customers, you know, we see that they. They often when they are thinking about where to. Do work. They often start thinking with how do I get more people on the phone, right? How do I connect with my customer? And that's sort of task one. Once they feel like they've maximized that they shift into what do I do to maximize the on call conversion and sort of lifetime value of the customer to your point. And in that role, often they start with some manual things, so manual QA and, you know, and then they shift as they mature into these sort of more technology enabled solutions. So part of this is also sort of, you know, Getting companies up that maturity curve, you know, because you know, if they're, if they're not even feeling good about their answer or they're not even feeling good about the people on their team, they're not even ready to go have every interaction and interpreted for them. It feels at least to us, it feels like, so do you think that's fair? Do you think there's still sort of a lot of, you know, maturing that needs to happen in this organization? Or do you think even sort of very early teams should be using this technology as well?
Marc Bernstein:Yeah, well, I think first, the fact is that. Fewer than 15 percent of sales organizations today are using some form of real time guidance for their agents. So, you know, 85 percent are either saying you know, we're not ready for, it's not ready for us. So I think that's, that's the first fact. I think that it absolutely can be useful for less mature organizations. But the thing is, less mature organizations often don't know what to scale. So, that's kind of a big part of the power of the technology is it's almost like a, like a machine gun or something. Where, you know, it, you put a tactic in and then, you know, you're able to spread that around at a, at a, like, rapid rate. Terrible analogy, but you get the point I'm making. I think that That's like the power of the technology is that it takes behaviors that you've identified that are going to make a big difference for your customers and ultimately for your bottom line and allow you to amplify those technologies and make them, those behaviors and make them happen more often. And I think sometimes early organizations are still just at the early stages trying to figure out how do I get my phone to work? How do I have a basically effective customer conversation? So they're just trying to get the basics figured out. So I guess my ultimate
Alex Levin:that sign like either when you're prospecting for customers, like, what do you look for to know that they're mature enough? Or like, if a customer is thinking about it themselves and trying to decide on their 2024 priorities, like what's the sign that they're ready to start really investing in this kind of tech?
Marc Bernstein:there's no one sign and it's funny, Alex you know, our sales process has over 60 milestones and the milestone is some sort of action that the buyer takes to indicate, yes, this is moving in the right direction. We should continue to pursue the process together and every sale. Has all, ultimately all or almost all, sometimes you'll skip a couple, but almost all those milestones complete and that's even things like do you know your paper process? Like, do you know how your organization takes, signs a contract and who the signer of the contract would be and where the budget comes from? And do you have a cloud based telephony system, or are you still on like a legacy hard phone system? Those are all the sort of questions that indicate whether somebody is ready or not. I think that, you know, my recommendation to folks would be you don't know if you're ready. We don't know if you're ready until we talk. And I think that when you talk and you kind of see the solution and we start to map out different ways you could use it, you'll either say, Oh yeah, like this is, this is our next thing. Or you'll say, Oh, this is like three steps later. But you know, the more that you have that dialogue, the more, you know,
Alex Levin:So, I mean, we've seen very happy customers with Balto and sort of recommend it, particularly when. So Regal already does sort of post call transcription and, you know, there's ability for people to look at sort of that, but when customers are starting to push beyond that and they say in real time, I need to be able to make sure, you know, one, my agents are saying what I want them to say to, you know, I want to understand in real time how to suggest different things, you know, than, you know, than what they're saying for, for instance, that's when really goes beyond, you know, In my mind, the sort of what's, you know, the vanilla conversation intelligence that's become, you know, very available into something that's really quite differentiated and something that bolted us very well, you know, very few companies are able to do this in real time today.
Marc Bernstein:yeah. Thank you, Alex. It's, it's you know, when we invented it back in 2017. It was a major leap and, you know, very fortunately we invested in that tech, not technology almost exclusively for the first five years of the company. And, you know, now the company is seven years old and we're looking at the next era of the technology. You know, what are folks able to do with generative AI? It's very real. And over the past year, we've probably had. I think this past year, we have had the most progress on our product roadmap and tangible progress that customers can feel than we've ever had before in history. And I think part of the reason is that before these Gen AI models came out, if we wanted to create a model to detect if a customer is upset, that would be Us gathering up 500 hours of call recordings, labeling the recordings with a contractor force in South America running a model, testing the model you know, selecting the best model, then rerunning it in production to see if it performs like accurately in a real environment, then productionizing it and getting it out like to our customers. So that would be a six month process. Now it's one query, one set of prompts to any of these you know, awesome third party LLMs or even some of the open source or our own LLMs. It's like one prompt. So the ability to be able to create a lot of product quickly, it's, it's been, it's unprecedented opportunity. It's never happened before in history.
Alex Levin:So, so, you know, we have a couple of minutes left, like, you know, looking forward, what do you think is going to happen in this industry? Like, what, what should customers be thinking about over the next couple of years?
Marc Bernstein:Well, I think that the biggest thing we're going to see. Is that over the next three years, AI is going to get so sophisticated that it is as intelligent or more intelligent than a average person. And that essentially means, and folks will call that AGI. And there's different definitions of it, but AGI artificial general intelligence. I think we're going to see that within three years. And what that means is that, you know, now we have a relatively fixed workforce at any time. People are always coming into the workforce and out of the workforce, but it's roughly 140 or 150 million in the United States. And if we want to increase the workforce, we now need to tell people who aren't working, Hey, we really need you to start working. We need to get immigration and add more people to the population, or people need to be born and mature into the workforce. That's how you create. Like, you know, how you have more working capacity, but now we're essentially going to be able to create, when we get to AGI, unlimited numbers of workers at like a fraction of, of the cost of what a human needs. So it's going to be a massive, massive disruption to like the labor market to the, you know, the, like our economy, our economy, our like societal norms, and there's going to be a lot of change. It's going to be happening over the next call it five to 10 years. So I think that the thing that I would predict here is not only will we have AGI within three years, um, but that people are going to need to, to really make an effort to keep up with the technology as much as possible. Because it will get harder to keep up with the technology because the technology will get better faster than we can learn it. And we're almost at that pace where new technologies are coming out every single day. And if you wanted to, you know, get your hands on it, by the time you get your hands on it, the next version's out. I think we're going to see more and more of that. And the people who are able to really invest in understanding it and using it. And use it in a useful way, I think are the, the folks who are going to be able to build really big businesses and lead amazing companies.
Alex Levin:Yeah. So you were saying AGI in three years, do you think it's indistinguishable from a human at that point? Or even sooner? Is it in this, like, if I'm having a conversation with what I think is a human online on the phone, forget text, like, at what point will that be, you know, will it be that AI is indistinguishable from a human?
Marc Bernstein:Yeah. Within three years, I think that if you look at what causes it to be distinguishable, indistinguishable there's vocal traits and then there's reasoning traits. So vocal trait might be. The amount of time it takes to respond to you, not having an excessive lag a vocal trait might be the natural intonation that we use, or the little stutters or the likes and ums and the filler words it might be just simply the volume you speak and modulating your volume in a very normal way. We're getting very, very close to being able to. Make the vocal aspects of AI nearly indistinguishable from a human, which really means the last bit is the reasoning capabilities, which is if you tell a joke and the joke is sarcasm and it's very dry. Will an AI be able to get it? Well, first of all, people don't always get it. Think about the number of times where someone tells a dry joke and then someone goes, Oh, I'm sorry, are you kidding or are you
Alex Levin:Yeah. Was that a joke or was that real?
Marc Bernstein:Happens all the time. So how do we get an AI to be as good at that reasoning? As good at the context? That will take a little bit. But I do think within three years, if you're having a standard conversation, it will be Indistinguishable, but that won't be in the mass market for regulatory issues. And you know, will the AI have to disclose, hi, my name is Alex. I'm an ai, how may I help you today? And then will customers accept that? Will they be cool with that? If the AI
Alex Levin:you think one of the standards will be disclosure? This is AI not a human.
Marc Bernstein:Yeah, you, I think you're gonna have to, I think actually that'll be a important almost signature throughout our society, and you can see how, how much people lean toward those forms of disclosure. Remember when. You know, the pandemic first hit, you know, there were the vaccine passports and people really wanted to, like, be able to distinguish safe from not safe, familiar from not familiar you know, do you have the vaccination credentials or, or do you not, I think almost like leaning toward wanting to do that sort of segmentation is like a natural human instinct. And I think we're going to very quickly want to know, are we talking to a person or are we talking to an AI so we can. Feel secure that with our own operating models, if that makes sense,
Alex Levin:For a long time I, my understanding was you could just ask it a simple math question, but if I understand now, there's some models that are starting to be able to answer some AI models that are starting to be able to answer simple math. So it's harder to tell if it really is a AI or not. But yeah, today most models you say, you know what's three plus six and we won't be able to do it for you.
Marc Bernstein:the recent Gemini release what the Google researchers put in their paper that this new multimodal model called Gemini which basically means that it can multi modal meaning that it can do a bunch of different types of functions. So it can analyze text, it can analyze voice, it can do computation. And they show this awesome example of an AI solving a physics problem, you know, like you know, it's grading a student's paper and the student is, you know, doing an illustration to show the, you know, some sled going down a hill and it's, the student is writing out in handwriting their answer and the AI is picking up the illustration, picking up the handwriting, Converting it to some concept, the illustration, to what that really means. A sled going down a hill at a certain speed. The handwriting for text. And then running it through its math model and saying, here's where the student made a mistake on their paper. You know, that was released by, you know, the example released by Gemini a week ago. Imagine what this is going to look like in the next three years.
Alex Levin:Yeah, much better. So should we all like leave our day jobs and go learn how to be AI engineers? Is that the future?
Marc Bernstein:You know, I actually think the future is what some AI researchers will call an era of plentitude, which is that companies will get so good at building stuff efficiently, that the costs for, you know, everything that we enjoy in society will, will go down. And that'll actually give people the opportunity To do things that are less of a grind and more fulfilling. So the what I would recommend to people is stay up on technology. So you can be a part of this new era of the workforce and. Lean into your humanness, learn math, learn art, learn music you know, write poetry go do comedy, go do plays. I think that we're gonna see the things that people do that are intrinsically valuable because there's some human aspect that's emotional, that's flawed, that's creative. I think that that is how more and more of us will be spending their times. And I think it'll be a really wonderful world. That won't happen until we go through quite a bit of disruption. First,
Alex Levin:Yeah. Yeah, I agree. You know, AI is a deflationary force. So by and large will be very good for people. You know, every time there's been a massive transition in whether it's a a laundry machine or a TV or the internet, like there's been sort of. Naysayers worried about sort of eliminating lots of jobs and it may have changed the jobs people do it But net net societies become more efficient and created more jobs not, you know, not the opposite So thank you for joining me today Mark if somebody wants to reach out to you. Where should they go?
Marc Bernstein:they can visit our website at balto.ai, b a l t o.ai or they can connect with me on LinkedIn and I'm on linkedin.com/in/baltoceo.
Alex Levin:Thank you very much
Marc Bernstein:Thanks, Alex.