The AI Strategy Gap

You know we got it.

Hey, and welcome back to the Tacos and Tech podcast. I'm your host, Neal Bloom, and I'm thrilled to have on two longtime friends here in the startup ecosystem of San Diego. 2 of the co-founders of Codra AI, Grayson Lefrance and Chad Laurie. Hey guys, thanks for being on.

Thanks for having us, Neal. Great to be here with you.

What's going on, Neal?

It is good to have you guys on. You guys have been heads down, and I'm lucky to have pulled you away, uh, for just a few minutes. Or maybe I pulled your, your AI twins away, uh, we'll see. We'll do a little Turing test here in a second. But, uh, you guys have been building, which I love to see, uh, building, building tools but really helping companies build. Do you want to give us a high level real quick, uh, Grayson, what Cadre AI is?

Yeah, what we're doing at Cadre is we're really a strategy and integration firm when it comes to AI. So we're working with companies to help them get really clear on their AI strategy, really trying to help them think more about what are the business problems that if we can solve or improve with AI will drive the biggest P&L impact and allow the company to grow revenue faster and more profitably versus, I think a lot of people are kind of chasing AI tools and we've really seen that that doesn't drive a lot of impact and you can argue creates distractions. So we usually start with strategy, but then really what we want to do is be their AI team and be that horsepower where we can build a team around that company that is strategy, engineering, data, product and research, project management. The challenging thing is to like execute with AI really well. You typically have to integrate a lot of systems and tools and you need a lot of different skill sets. And it's difficult for companies to go make that investment and attract the best talent and have the best, you know, view of the playing field. So since we're, we're doing this across a lot of different organizations, we have a very good vantage point and we've done many of these things before. And so we're that kind of dedicated cadre of experts that can help them both to get clear on the strategy and then to actually realize those, those business outcomes with AI.

I love it.

Very succinct. And we're going to get to more like tactical how that's happening.

No one's ever told me I'm succinct, Neal, so thanks for saying that.

You got it. You can take that one today. You— let's take it back a little bit with you because you have done the service side with businesses before. With Power Digital. Uh, so take us a little bit, give us the short arc of, you know, Power Digital and what you learned from that that you're kind of applying forward into this.

Yeah, so Power Digital, we grew into the largest privately held, or one of the largest privately held performance marketing agencies, and started that business just bootstrapped in 2012 and had a lot of growth. And we really focused heavily on working with consumer brands to help them scale revenue faster and be more profitable. And we did a lot of tech enablement of that business with the Nova platform and, um, AI enablement. And we actually sold that company originally to private equity, as you know, in, in 2019. And then we were a platform company and rolled a meaningful amount of equity forward, took some chips off the table, and we did 4 acquisitions with that group and then did a second transaction or second bite of the apple and 2022. And in 2022, I believe it, it was a, a record-setting valuation and one of the largest, you know, private deals that's happened in that category. And I, I think a big part of the reason is that we had really elite financial performance when it comes to growth rate and EBITDA. And a lot of that was due to what we had done with AI and tech enablement, specifically around how we go to market how we give value in advance to clients, how we automate things, how we allow humans to work more strategically and faster with technology and AI. And so really, I mean, Cadre is the same business model. It's like we sit at the intersection of big technology. So Anthropic, OpenAI, all the tech players that these tools are not that simple just to implement and that requires real expertise. And then the companies that need to adopt them. And we really represent the companies. And so it's like, what's the business problem that if we can solve this, we'll drive the biggest impact? And then we help them create that plan and implement. Just like at Power Digital, we did the same thing, but really focused around customer acquisition, customer retention, lifetime value. Whereas at Cadre, we're touching every part of the business. So it's probably less so on like sales and marketing and more operations and how they, manage their people and how they actually do the work and how finance and accounting works. And, you know, everything from recruiting talent to, you know, keeping talent and doing the work. And really, how can we use AI to transform those businesses? So lots of lessons learned at, um, Power Digital. I'd say the cool thing with Cadre so far is I'd say 90% of the things are easier, way easier in this business than that you know, than a year and a half in or 2 years in at Power, um, except for, you know, getting clear on how to solve certain problems. So with AI, there's a problem and there could be like 20 ways to solve it with AI. One could be really fast, scrappy, probably not the right long-term. Two, there could be a system that does that, that maybe works for you or doesn't. And so we've had to really invest in this product and research organization so that we can very quickly figure out What is the best way to solve this problem for this customer? And that's going to serve them best the longest. But, you know, Cadre is off to the races. What we did in year 1 revenue was like, I think, year 4 at Power Digital. And what we're going to do this year in revenue is like what we did that first transaction year in 2018 when we were running a process and ultimately sold to private equity. So that shows that you learn a few things having done it and from past mistakes and also the amazing momentum and how fortunate we've been to attract really incredible people that are excited about what we're doing and just have a, I think 60-person now, Chad, team that's excited, fired up and wants to do great things.

Amazing.

Wow.

Well, and I got to imagine, and maybe we'll touch on this a little bit, if you were to start a marketing services performance marketing team again, that would look very different in the world of AI as well too. So we'll touch on wherever, where that, you know, AI has got its fingers into. But let's take it back with Chad as well. I, I remember work a few instances working with you, I think through some hackathons and then again coming in contact when you were leading a community in the blockchain community, specifically Ethereum San Diego. But what I've known you as a community builder and a tech builder. And that's obviously come together here in a few different ways. Take me back a little bit to your path towards tech here.

Yeah, I think first time we met each other, Neal, you were a judge at one of these city hackathons back in 2019. I was building this AI system to, you know, help service roads here in San Diego. I quickly realized that, you know, trying to get into government tech was, was the wrong thing for someone that wants to move fast and break things.

Was your project the cameras on buses mapping?

Yes.

Yeah.

So that was, that was kind of my first, first attempt at building something and getting it to market and piloting it. But it was my first time building really something practical with AI. And from that point on, I was interested in just, you know, can we build a successful company in San Diego? What does the ecosystem look like? I met you, the Startup San Diego team, some folks from Connect, and quickly realized that there's space to grow our tech ecosystem here and bring together some of the best builders and either use that selfishly to find a co-founder or to get better ideas, but ultimately just be with peers that I enjoy hanging out with, which are individuals that like to write code and like to go surf, right? And we've got a good combination of that in San Diego.

That will never hang out with me outside of work is, you know, I can't write code, so I'm—

I think, I think we'll get into that. But I don't write code anymore either. Right. We've got—

Yeah, come on. Your Cloud coworker writes her code.

Better write code. So actually, Agent Grayson's writing some code too in Cloud. You should see the POCs that he builds in his office. Pretty impressive stuff. But, you know, the community building led eventually to this community called SDX. And this was right around the time ChatGPT dropped. And it was the moment where I was trying to figure out where to go next. You know, spent 3 years in the blockchain space, very interesting space. I think we're going to see a convergence of blockchain and AI moving forward. Coinbase is doing a lot of interesting things with agent payments. And I've got some pulse still in that arena. But it was just so much— it was just very obvious to me that large models were going to be this massive value driver that everybody could touch and utilize to drive value in their personal lives, but also to just drive business value. And ran SDX. It's been 3 years now building up that community, bringing smart people together, partnering with large tech companies like the Anthrops of the world, but also local companies like Qualcomm. And then through that, you know, started figuring out how do I actually make money myself with AI, consulting with some local firms here in the private equity space. And ultimately that led to a meeting with Grayson where he had a very clear vision on how to break into this market, you know, how to add value with AI and where it really matters. And, you know, had a vision that I was like, dude, let's, let's do this. This makes too much sense almost. And he actually talked me out of a couple other things, you know, that I was pursuing at the time. But hopefully that rounds out a bit of the backstory.

Yeah, I love it. What did it originally look like on paper? What did— what the original idea that you guys came together, the three of you, I think it was three or four of you founders together that came together.

It was originally three and then we added a fourth kind of like a couple of weeks in. So it was Chad, myself, and then Riley Strickland. And then, um, uh, Ben Shapiro kind of joined us, right, you know, a week or so later. Um, it looked like chicken scratch. Have you seen my doodling? Actually, I'm a pretty good doodler. Um, but, uh, really it was like, for, for me, what I saw was I was kind of transitioning at Power Digital into the chairman role, and I was trying to decide, like, am I gonna retire? Am I going to do the investor advisor thing. And, you know, neither of those things were for me. I wasn't ready to retire and, you know, do that thing. And then the investor advisor thing, it's just not— I want to be more in it and hands-on. And I saw this huge opportunity where AI was so hyped, but I was like, I have not seen AI do shit in most businesses. And so started talking to a lot of different kind of middle market CEOs and executives. And just asking them like, hey, do you guys have a clear AI strategy? I'd say I have 100, maybe 1, probably not even would say yes, honestly. And then asking them like, hey, is AI showing up in the P&L? Like, are you seeing measurable financial performance or financial movement in your business from AI? Maybe 2 would say yes. And usually it was around either some sort of AI coding practice or something AI RevOps oriented. And so I was really like, wow, this is exactly like performance marketing. The reason why no one's winning with AI is it requires A, time, B, expertise, C, a lot of different points of view, you know, business, what are the problems we're solving? Okay, are they solvable? And is it realistic to solve that? Okay, let's build a little financial model around this so that there's clear goals and we can evaluate risk and whether this is worth doing. And then, okay, how are we going to engineer a solution that will do this and what's the best way to do it? And then change management and getting people to actually use it. And it's like, that's hard to do when you're running a business. So really saw this need for services, which I love. I like businesses that actually are profitable and can self-fund themselves so that you can use the profits of the business to fund growth. And, you know, hopefully you build it and it's something that is really sustainable and is growing and profitable, and there's going to be a lot of people that want to buy it. So really, it was a lot of the learnings from, from Power Digital and mistakes that were made there in that business model, but applied towards AI. And so, you know, when I was kind of looking to do that, had really good third-party validation and then was looking for the right players. And, you know, Chad is a great example. Is very complementary to me in that for a couple reasons. One is very, very technical, uh, very strong network with the technical community here, which we're trying to really build San Diego as our hub in the U.S. We do have team members in, you know, across the country, in New York, you know, all the way to New York, um, across the U.S., but we're trying to get a really strong hub so that we can build a really strong brand, uh, with the talent community here. And we think San Diego is as good as as good as any to do that. And then also in Latin America, so that we can have a real global delivery system and be able to, you know, work 24/7 for clients. So, you know, meeting Chad, it was like, this is a great balance. And I think the other aspect with Chad is like him and I probably look at AI very different. I'm much more pessimistic on AI. And headlines. And, you know, I think it's going to move slower than people think, not because of the technology, but because of the actual, like, implementation and ability to get humans to change how they operate and how many different people within an organization need to be bought into an AI initiative for it to actually succeed and how hard that is. Whereas, you know, Chad is more of a, uh, technologic— whatever you call it— technologist, uh, early adopter maybe. Whatever the right word is, and, you know, probably believes it's faster than I do. So I think that creates a really healthy balance. And I think that's what we've rounded out well with the founding team. When you look at, you know, Riley and I coming from more kind of business operator backgrounds, and then Chad and Ben coming from more, you know, on the bleeding edge of technology backgrounds.

I love that.

But the plan, the plan, Neal, it basically just said build the most valued and respected privately held AI strategy integration firm that somebody wants to buy for a couple billion dollars and do it so that it's fun and you're doing it with badass people that are partners into where all of our equity is owned by people working in the business. There's no outside influences. And, you know, we can invest those dollars back into growing and really, you know, be the most respected by the talent community, by clients, and by big tech. And that's, uh, you know, what we're working towards. And, you know, so far so good, but lots of room for improvement. And, you know, we're getting better each day. So that was pretty much all that the plan had on it.

Now it's up on our wall, actually. Yeah, that plan, or at least a bit of it.

Just the destination. Yeah, the plan.

Totally. Well, and you— I mean, having, you know, very different backgrounds, insights is what's helpful around this, right? Having, uh, one, Grayson's, your network of EO and YPO members gives you that insight to know how long it takes to onboard systems, right? Like, I'm sure you've probably heard, I don't know, 100, 200 EOS implementations talked around forum groups, right? Let alone how do you redo every tool and every system around that. So, and then Chad actually building it and turning that into businesses as well too is a realness as well. So I think that's the beauty of co-founders is the diversity of pretty much everything— networks, insights, lived life, you know, all that. So what's it been like now? Like, what is— tell us like what a day-to-day is like when you work with a business. What are the ones that you first sought out to reach versus that you're working with now? And what is their expectation of like, AI is going to change our business versus what it really looks like? Maybe Grayson, you kick that off.

Yeah.

So a couple of things on that. So one is like strategically our approach was like, let's start pretty opportunistic on the clients we work with. So basically we're like, let's, let's get on a phone with any business leader that wants to chat, not saying we're going to work with them and we'll still do this. And let's have a convo. Let's understand where they're at. Let's understand what business problems they have. Let's understand what type of person they are. And if we think, wow, we could really help them and we believe we can make them a lot of money. And they seem like good people to work with where we could truly be partners in this journey, then let's work with them. And I'd say that's still the approach. And I, you know, part of it was like, we didn't want to have these really hard preconceived notions on like, well, this type of business is going to be best or this type of business. And honestly, there's been some end markets that we've kind of wandered into where we're like, holy shit, AI really moves the needle in this space. And we would have never guessed that. I'd say where we've landed though is we're pretty focused right now on kind of mid-market or lower mid-market, I'd call it B2B services businesses or tech-enabled services or SaaS or B2C services businesses. And the reason why is these companies that scale very heavily with people and headcount, they're really ripe for AI disruption. So typically those types of businesses actually lose operating leverage as they get bigger. So they're usually more profitable when they're small is what I mean. And as they reach $50 million of revenue, $100, $200, $300, they usually see margins decrease. And the reason why is their non-billable functions and payroll get bigger. So the accounting and finance function's bigger. HR is bigger. Recruiting is bigger. Sales is bigger. Marketing is bigger. And their middle management layer grows. And that is the hardest thing to scale on those types of businesses is like finding great middle managers. That can get the most out of people, can make people loyal, can develop them is very, very difficult. And so AI is really good in those businesses of allowing them to go from a ratio of where maybe they add $2 million of revenue, they have to hire 10 people to add $2 million of revenue. And now you got to hire 3. And now you can have a smaller, more elite team that can work on more strategic work because you're able to do some of the more tactical stuff. So that's really where we're focused. We've learned a lot. We've made a lot of mistakes and I had made a lot of mistakes with AI at Power Digital previously. I mean, what I mean by mistakes is spent a lot of money and time on stuff that never drove the ROI desired. So I think that's helped us. And what we've really learned is as much as like our desire is to start hammering nails and turning screws for clients and implementing these systems, like that's what we want to do. But we have to be a little patient. And so what we've learned is that if they don't have a really clear long-term vision and strategy for AI, that we need to help them get that first. And so for most clients, we like to start with a really low-risk project, which again is not what fuels our business. We don't want small projects, but that's where we start. And usually we start with benchmarking where we use this maturity index that we have. To really show them where they are and how their team views AI and where it's being used. And I'll tell you, there's massive disconnect between what the C-suite thinks and what users are using. And we have a whole scale where we've now ran that across thousands of businesses and we can show them where they show up. And we're starting to get to where we can say, hey, based on you being here, you have a higher probability of being successful based on how your team members view this. And then we usually go from there and do these really deep workshops where we meet with the key department and functional leaders and dive into their problems. And, you know, in sales, why aren't you able to do 150% of plan or 200% of plan? Is it deal flow? Is it concentration within your sellers? Is it the sales cycles takes too much time and you've got bottlenecks around proposals and dynamic pricing? Is it the handoff? You know, what are those core problems and how can we go attack those? And after we do all those deep dives, we deliver them a use case library and we walk them through and we're like, here's usually it's 30 or 40 use cases where it's like, hey, here's a business problem you have that you've shared. Here's all the info on it. And here's how we'd solve this with AI. And here's how likely we think this is to work. And here's roughly what it would cost. And if it was us, we would do this either short-term, kind of midterm or long-term. Some of the stuff, it's like, man, this would change your business and AI can do this, but it's going to cost you an obscene amount of money to accomplish that. And let's put this thing over here on the parking lot because we believe that the technology will be there in 6 months from now where we could do that fast and at a fraction of the price. So after we go through that use case, we usually will deliver them kind of like a 3-year vision that we kind of co-author with them. Of if we're successful, what does your company look like, feel like, what's the financial impact of this AI transformation? And obviously that's like a website. It's like a living, breathing doc. It's not, it's gonna, we update that every quarter as we work with clients, we refer back to it. And then based off that, we help them build their 12-month roadmap. And they could take that 12-month roadmap and build an in-house team. And we've helped clients do that. I'd say that most, that's not the best path, but if that's their preference, we'll help them do that. Um, but about 90% of the time, we've now gotten really deep into their business. We've built a great relationship. We've demonstrated our expertise and the caliber of our people. And then we help execute on that 12-month roadmap, and it's all measuring. It all ladders up to the longer-term vision. So that's what we're really That is our recommendation for most businesses. It's not cookie cutter though. It's like we're meeting them where they're at. And there's definitely some companies that are a lot further along than others. But like, if we don't have that true north, this is just noise. We're just doing random stuff. And I could almost guarantee you that without a really thoughtful strategy of what's being done and how it's being attacked, that you're going to waste money and you're going to really limit the impact. And so taking that 30 days, we can do those in 30 days now. To do that. A lot of times the company can't move that fast, the client, so it's 30 to 45 days. But doing that, taking that time to do that, and I'm one of the most impatient people that you'll ever meet, and I hate spending money on stuff that's not tangible, and I like fast and scrappy. So if I'm telling you this, you know, it's— you can believe it's the right thing to do because, you know, I'm usually more like, let's have a really simple plan let's go after it, let's pivot and learn. But doing that is the best way to do it. And I would bet a lot of money you will not be successful at the level that you should if you don't do that. So that's where we're typically starting.

Can you give us a, paint a picture a little bit of like the kinds of industries or kinds of companies without, you don't say names, but just like, are we talking?

Yeah, so I mean, financial services. So we're working with a lot of private equity firms. Mortgage, business lending, banks, and some of the more heavily regulated industries to, you know, insurance services. What else, Chad, comes to mind?

We got distribution companies, some manufacturing companies. You know, we've got like 60-some clients, 70 clients, I don't know. We lose track, but I think there's a heavy concentration in the financial services and You know, you think about any industry that has a lot of paper movement and a lot of handoffs, like Grayson mentioned, very, very prime for automation or, you know, AI augmentation. Similar too with like anything supply chain, you know, heavy customer service, heavy operations. Anytime you're moving information from one team to another, you can imagine that there's going to be gaps in that process that make really good opportunities for us to inject AI and roll that out across teams.

Yeah. And within our business, the way we work is we've got kind of our brand and demand team whose job is to help us with our thought leadership and ultimately generate deal flow opportunities that we can vet. And it's very much a mutual vetting. We're vetting the prospect as much as they're vetting us, and that's how it should be. And then there's the strategy and sales team. The sellers are usually just, it's, they're not salespeople. They're more, you know, AI business consultants. It's not, they're not prospecting. So they're really trying to determine, can we move the needle here? And then the strategy team is usually doing these initial projects. It's kind of a loss leader for us and laying out the strategy. And then it goes into solutions. And within solutions, we have these pods. So if you're a client, you're going to get put on a pod. These pods are kind of verticalized. And a pod has a pod leader that's usually a very business-savvy, super experienced person that really understands AI and really understands the problems AI can solve well and is great at communicating and kind of change management and education. And then you've got usually a forward-deployed engineer, which is an engineer, but that's strategic and more client-facing. Um, usually have a couple other, uh, you know, back of the house engineers that are actually executing on this stuff, and then usually one or two AI strategists and a project manager. And so what we do with the pods is each pod has its own P&L, and they deliver, you know, we play matchmaker with them. So over time, those pods will be fully verticalized where the Private Equity Pod will only work with private equity clients. You know, right now they're concentrated in that regard, and there's, I think, 6 pods right now, um, and they're concentrated by, by end market so that we can share kind of lessons and learnings. But, uh, it's not as if like the pod only does banking or this category, just based on kind of where we are in the journey and, uh, the current mix of clients.

Love it. The famous pods. It's known already, uh, your guys' pod system. Uh, Chad, take us through some of the tooling, I guess. Like, you know, like you're utilizing tools. What are the tools that these pods, uh, have that, you know, you use to just supercharge these, uh, the teams and the businesses?

Yeah, I mean, I think you, you can break down, um, the tooling by integrations, so connectors to various systems, right? AI is, um, it eats data, it needs data to be able to synthesize and make decisions. And so, um, first is, you know, what systems do we need to connect to and what are the integration points? Um, very common is your CRMs, right? You got Salesforce, you got HubSpot, you got smaller ones as well for some of the mid-market companies. You know, we use a lot of note-takers in our business to do work. And so connecting to a company's note-taking system, whether we implement one for them, roll it out, or connect to an existing one, there is like a treasure trove of data as a company builds up the amount of transcripts that they have. For Cadre, we've got thousands and thousands, and we do, we make a lot of decisions somewhat autonomously based on those transcripts. But other connectors, I mean, you know, ERP systems, you know, code bases, right? Anything where your data lives. And so that's kind of the first, you know, part of the tooling. Then you have the processors, or really just your LLMs. Call them agents, you can call them automations, but what's actually pulling in that data and making decisions and then pushing it out to another system, right? Customer service, for example, is an easy one to talk about. You know, company might get thousands of requests per day, right? So we tap into that, call it HubSpot, where those customer service requests go, and we need to triage those and figure out, all right, which ones are high priority, which ones are, you know, more technical to send off to a technical team. Can we actually answer the technical ones if we have a large enough knowledge base of technical Q&A questions? And so, you know, not to get too deep, but the tooling is really how do we get data from point A to point B? How do we synthesize and transform that data with AI? And how do we serve it up in a way that's tangible, that actually brings impact? It saves time because we got from point A to point B faster. It allows, you know, employees to just make smarter decisions. It increases the throughput, for example, in loans, right? How do we get a loan from intake all the way to underwriting in a timely manner? And so you can think of all the different components. You know, you got your loan origination system, you've got your underwriting system, you've got your pre-checks, your operations team, you've got your sales team that's going back and forth. All those are systems that we need to connect to, figure out the right business logic, usually an SOP. Train an AI system to be able to manage that SOP and then be able to orchestrate data from point A to point B, um, in, in a timely manner.

Yeah, and one of the cool examples, like, for Cadre and, like, AI system working in our business that's really impactful, that's probably relevant to a lot of companies, is for most services businesses like us, like, your knowledge base is your Zoom calls, Google Hangouts, phone calls, your emails with clients, uh, or prospects, your internal Slack. And so we have a system where all those are feeding into our data warehouse and then ultimately feeding into an AI system with a UI on it that will flag every day, this client is, uh, a risk, right? And here's the flag. This client is delighted. They actually said these great things about you. Boom. Here's the clip. You should use this as a testimonial and ask them for a case study. Or referral and everything in between. And then there's like privilege levels. So that can go up to a pod leader, could go up to chief client officer, to president. It will also take every call that happened, digest those calls down each day and do a 10-minute, 5 to 10-minute podcast that our president Keith or our chief client officer Sarah can listen to at the gym or driving in to get a full lay of the land as to every client touchpoint that occurred. Or our chief revenue officer on sales. And so when Chad's talking about like call recordings, like everyone thinks, oh, AI call recordings are cool. And Chad will tell you, I freaking hate these note takers. I think we have a whole generation of young people now that can't drive from their office to their house without using Google Maps that are the same way with a meeting. They can't just take some notes and they have to go query it and it gets way overdone. But where I think they're really valuable is in those like automations and intelligence systems. Because what I described has been like, was a long time problem for me at Power Digital. Power Digital has like 500 clients. And we would have these account managers fill out like a sentiment dashboard once a week. And is it green? Is it yellow? Is it red? And if it's green, hey, great. We should be getting referrals or case studies or expanding. And if it's red, it's like, well, how do we fix this? How do we turn this around? But A, to actually get those hundreds of client services people to actually fill it out each week consistently was rare. Two, even with a very clear defined standard, you know, Chad has rose-colored glasses on and Grayson is like very pessimistic, paranoid, and negative. We're going to rate those very different. So the AI system has no bias. Like it's going to grade them based on how we are training that system. And now we have a security system watching every interaction we have with our clients. Even things like, hey, is the CEO like now only showing up to like 20% of those meetings? That could be a good thing or a bad thing. That could mean that he trusts what's happening. It could also mean that he thinks these meetings are a waste of time and we need to figure out how to, you know, gear those client touchpoints to be more relevant. So like every service business should have that type of system and AI makes that super possible. And we've built that for ourselves, but to now we can deploy that pretty easily for clients and they get a lot of the benefit of the last 6 months of training that we've been doing to that system. Now they're gonna have slightly different criteria and we have kind of like this configuration module where we could say, look, for us at Cadre, this is important to us, these types of things. It may not be to you. Um, what are those important things, right? So a lot of what we're building is products. We call them blocks, and they live in this, like, Cadre One system that we have. And our goal is that if I solve a problem for Cadre, that now our clients get the benefit of that, and we can redeploy that system faster, um, and it's already been trained, and we can configure it fully custom to you, and it's yours. You don't need to be working with us to have it. It's yours. But these blocks— like, our concept is that every company is different, and instead of redeploying custom system every time. We're building up our menu, and then when we go into a company, hopefully if they've got 30 amazing opportunities with AI, we've done 20 of them before, and we can deploy the work and the training that we've done in the past so that they can get something better, faster, stronger than if they just went and did it themselves for the first time. Um, so that's really like the core that we're focused on. Now for us, it's a constant battle of like, we have like 3 engineers, I believe right now that work full-time on our internal technology and systems. But then that tooling is getting better. Every client we work with, it's getting more robust and bigger. But for us, it's like, we almost sit between this like profitable potential private equity backed service business and what could be a venture business. And we've had a lot of interest from VCs wanting to invest in Cadre. And we've told them no thank you. Um, we've also had that interest from private equity and we've told them like, no, there's nothing we do with your money at this point. And if I ever come back and ask you for it in the near future, you should probably run away because it means we got some real problems here, right? So, but I mean, our mentality is we're not distributing any profits right now. We are putting all that money back into the business to get the best team and to really build that moat of these blocks and all these different AI use cases that are getting better by the minute that our clients get to harness. So I think that's when our business really becomes supercharged. And the great thing is, like, of the 90 problems we have at Cadre that are on our list that we're working on solving for ourselves, the vast majority of the clients have those same issues. It's easier for us because we happen to be an AI business. We're also small. We have 60 people. It's a lot easier for a sub-100-person company to implement AI and do the change management. You start getting up into the mid-market with thousands of employees, 2,000 employees, AI transformation is way harder. Now there's obviously more historical data to train the systems on, and that part is better. And ideally there's a cleaner data infrastructure, but usually there's not actually, you know, you've got a mess from many years. But the change management, like, can't be understated. Like, that is really, really hard. And the bigger the company, the harder it is. And that's where, when we do those index maturity indexes, like, people are scared of AI. Employees are scared of AI, and they're a lot more hesitant and skeptical than the C-suite thinks. And so you're fighting, you're kind of swimming upstream with that. And that's been one of the more surprising kind of aspects, you know, for us with, with clients.

Really interesting. So the humans are, you know, the slower, uh, adapters, uh, compared to Chad's like techno lean-in optimist. So if either of your positions of being super pessimists and super optimists started to switch a little bit of seeing how slow people are, but also seeing how quick tools are helping you guys build your business.

Grayson, I'm curious. Yeah, look, I mean, I think, you know, I'm a creature of habit and I've never been like the real technical tools guy, you know, like, you know, it's funny. I'll give you an example. Like I read every piece of information every day on what's happening still with the Los Angeles, unfortunately, Chargers and Arizona Wildcats and all this. Like Chad does that with like AI, like that's like what fires him up on like you know, so we're different in that regard. I mean, look, I mean, I was like a heavy, just on a personal level, like ChatGPT user. And I would dabble with Claude a little bit and finally these guys were like, dude, you have to move to Claude. This was, I don't know, 6 months ago or 8 months ago. And they got me over the hump and now I'm like, holy shit, like this is insane. And I'm, you know, so I would say that Yes, like what you can do with AI and what you could do even just with simple systems like Claude, I'm like mind blown by, but where I'm skeptical is like, I look at myself as like probably a decent profile for a lot of executives that would be running these mid-market companies. And honestly, I'm probably younger than a lot of them and have been in pretty technical businesses, right? As a SaaS founder in the past, performance marketing. And to get me to go use like 7 or 8 different tools is like not gonna happen. Like I'm gonna have 2 or 3 tools that I use that I'm comfortable with. And so we're trying to solve for that. That's partly with the Cadre One system. We're trying to create a centralized place that is open and flexible and technology agnostic where you can control all of your AI in one place to some extent, right? So I believe, and I'm becoming a lot more bullish that we're gonna be able to do that. And I think that's going to unlock the velocity of impact from AI way faster because I believe that's one of the key inhibitors to why companies are having a hard time driving meaningful financial impact. I'm not at all skeptical of like, holy shit, look at what AI can do and look how fast the innovation is happening. I'm more skeptical of, I think it's a lot harder to apply that. To real business problems and actually realize the ROI than people realize, and then the media's telling us. But I bet Chad has actually come further in my direction. Um, so, you know, Chad, how— what do you— what do you think? Um, have you been surprised by how hard it is to get people to use this, and you know how stupid people like me are in terms of using this technology and how that prevents, uh, results?

Yeah, I mean, I, look, I think, you know, I was talking to one of my buddies from Y Combinator, and he's one of the visiting partners, and he said something interesting. He was like, look, if we froze models today, meaning like there wouldn't be a better, there wouldn't be like an Opus 5 or whatever, the next iteration of GPT, he said it would take about 10 years to diffuse the current state of this technology across, you know, all companies. Um, and after being in this business for, uh, year and a half, um, or a year and couple months here, uh, it's very true. Like, the, the, the technology is moving so fast, just people cannot keep up and processes cannot keep up, you know. So we'll get into a business and we've got an amazing solution, right? Some OCR system that can read any email and translate it, whatever, and we're ready to go and integrate it. But the processes are so manual, they're so in people's head, there's so much tribal knowledge that no matter how good the tech is, you're never going to have impact. And so you're kind of right back at the drawing board of, you know, digital transformation like 1.0, which is, man, you've got a screwed up process in this business. That, you know, has been, you know, chewed up, that has been changed, that has been undocumented, that has had many people's hands in it. And there's not one person that can tell you how the system works. And if you think about putting an AI in there and you think about an AI as like a fresh employee, an intern, there's no way they're going to be able to navigate that system. And so a lot of what we're doing is just trying to rebuild SOPs for companies which is what Grayson talks about, that strategy piece that we have to do first before we even get to the cool AI stuff that brings lift. And so it is challenging, but it's also— I'm very optimistic because it puts us in a beautiful position as a services company. And this article came out, actually, funny enough, Grayson shared this. I wouldn't expect Grayson to share this, from Sequoia. Came out March 5th, and it— the title says, "Services: The New Software," right? And the first line is, "The next $1 trillion company will be a software company masquerading as a services firm." And as our company has evolved, you know, yes, we're building a lot more software, um, but we also have this opportunity to diffuse it for potentially the next 10 years, right? We've got this massive arbitrage opportunity where, you know, we're going to be up to speed on the tech. Companies will never be able to keep up. That means we always have a job to deploy that, to diffuse that technology, to upskill employees, right? So for sure, definitely more in Grayson's camp than I've ever been. And I still am in the, you know, the Twitter echo chamber in the evenings. But the reality is, you know, vast majority of people have very little understanding of how these systems work, let alone how much time it takes to integrate them and adapt them and then get up to speed to actually see the ROI that they could potentially yield.

Yeah, and you were saying earlier, Neal Bloom, it's interesting, like what we're seeing is like companies all like the urgency is there with AI, the board's asking about it, but like most companies aren't diving in. Most of them like, and maybe we need to get better at selling some of it because we're like, what are you doing? Why aren't you doing all of this? We're talking about it's $100,000 a month maybe to do all this. And they're like, well, we're comfortable with $20,000 a month, right? Or whatever the example is. And so some of that might be we need to do a better job of helping them see the light and how we lay out the ROI cases. But I think a lot of it is like, there's a lot of skepticism from executives of like, all right, well, just show me one thing. And I think the challenge with that is like, okay, but to pick that one thing, we need to go do the strategy work. And it would be kind of dumb for us not to have that because very likely that one thing is going to connect to 10 other things. And at least it connecting to a few is what's going to drive really meaningful impact versus like, here's a $50,000 a month ROI. Like, who cares? Um, we want to drive millions of dollars of impact, right? So I think that that's been interesting because if you watch the media and the news, you would think that companies are just diving headfirst in. And I think even a lot of the success stories that you see, like everybody wants to be on the cutting edge of AI. And so there's a lot of lying in my opinion or exaggeration. And you've even seen some of these like companies that have raised money as AI company and then they've got everyone, it's just a bunch of people in India, you know, no AI. I'm not saying it's like that, but I think a lot of companies are overhyping how much they're investing in AI and how sophisticated and dialed their strategy is because it makes them look good. And so I'm excited to see that pendulum shift where companies are like, look, we're going to get more aggressive. We understand that some of this stuff's a short-term investment. Some of it should be more viewed as a capitalized expense, and we need to do this, and this is what's going to unlock us to win. Next year and the year after. Um, because I feel like there's a lot of like dabbling and a little bit of scared money with some of these companies when it goes to this.

Well, let's do something here. Let's, let's give someone an action item to start for their company, for a CEO right now. Like, turn on your AI note-taker for the company, or what's the thing that you think someone should just start to build that data and context mode or something for the next 30 days? Chad, what do you think?

Yeah, I think a note-taker is an easy one. I mean, ultimately it's looking, it's doing a bit of introspection in the company and saying, you know, where are the leverage points in our current company, right? Where can we turn on, you know, more revenue? Where do we think we can scale our company without adding a ton of headcount? And what processes just suck, right? There's this, You know, Toyota used to do this. It was called Go and See. It's one of my— I've been reading that book lately on assembly lines because I think, you know, things are in the coding world, we're getting more into this AI factory. And so I'm really interested in manufacturing. But anyways, one of the themes of what made Toyota successful is just go and see, right? Just go look at the business and like what is working and what's not, what is inefficient. And, you know, how could you just do that job better? First of all, without adding AI. And then if you have a good answer, you think you have an understanding of what that is, then you can start putting the pieces together and being like, well, look, we really, we really suck at, at our sales cycle is super slow, right? Well, why is that? Um, or we don't have enough information, or it takes us too long to synthesize information, get into CRM, get the next person on the call. Okay, well cool, what does that tell you, right? You need a way to get info quickly, synthesize it, and pass it to the next person. Can AI do that? And that's a lot of the work that we do at Cadre is we do these deep dives, right? We used to call it an audit. You know, now we call it a strategic engagement and we'll go through with CEOs typically or folks in the leadership team. We like to work with CEOs, CFOs, COOs, not really CTOs, funny enough, because they think they can do it all and that's okay. I think that way too. And we have them walk us through just, you know, these different pain points in their business. And sometimes we uncover a lot of things that they don't even know about. And then I bring in some other key stakeholder, you know, a VP of some sort. And then we go into that process and we just uncover so much of like, wow, we didn't even know this took us this much time. Why do we have 3 people just doing manual entry of data? And so I guess the one action item would just be go and see, right? What's actually working in your business? What's not? What are the cost centers? Can you transform those into profit centers? And, you know, hit up Quadra if you need help doing it.

Yeah, I think what Chad said is great. I'd say if it's something technology-wise, the first thing that I very rarely see is done well is like, what is gonna be your core LLM of choice for your company? And have you rolled that out? Are you measuring the usage and impact? Have you, you know, customized it to your organization? Do you have specific workflows to different roles? And most companies have not done that. If you want to just go start hammering nails, just do that. What I would say every CEO should do if they haven't though, is first is just appoint a committee. Find the right leader from each of your core business functions, get them in a room, let them know that they're part of the AI committee and that you're serious about this. Talk to them about how you all believe you should communicate the company's vision with AI to the employees and start talking about it so that people know it's for real. And then I would have that committee go and I'd say, hey, you're going to come back in one week. And I want the top 3 biggest business problems in each department, and we're going to put those up on the board. I'm going to find some AI smart person. Could be— I'll go sit in that meeting. Chad will. Could be someone from Cadre. Could be whoever you can find. Won't charge you a penny for it. And go through each of those and let that expert help you learn what are the bus— to spot the business problems that could be easily solved with AI. Like to me, that's what C-level needs to learn right now. And that's what I feel like I've been able to learn and get good at is not like the best user personally of AI, but like I can tell you when someone's describing a business problem, like, yep, that's right down the alley of AI. And that feels very easy and realistic versus, yeah, I'm sure I can do that. But like, dude, I would bet you everything I have that you're not going to get it to do that. You know, that's not actually going to work. And I think that's where executives need to go. And then how you attack those, I think if you did that activity, and that's probably going to give you 15 or so of these potential problems to solve, you're going to walk out of there with some stuff that gets you excited and gets you confident that, okay, this is how this actually could work in our business. Because I think for a lot of people, it's just like in the clouds and it's like, A lot of times they'll ask me like, what are real use cases? And I'll rattle off like 5, 6 of them. And they're like, oh, okay. Now I have the aha moment. Some that are very simple and some that are more sophisticated. So I think getting that committee and then really learning how to spot the problems that AI can solve well is step one. Not just like, let me put this tool in. Let me connect this. Let me connect that. Right? Because I think that That's a lot of, a lot of noise, and I, I don't think it strategically will serve you as well.

Those are great nuggets. I appreciate that, guys. Well, you are building away, helping companies. You're obviously powered by the food of choice of San Diego, the tacos. Grayson, you've been on the pod before, so we're gonna, we're gonna double-check and see if your answer has stayed the same. What's your favorite taco spot, and what's your order?

Had back then. I mean, I'm a San Diego native, so I like, uh, most of them though. I'm, I'm like more like the hole-in-the-wall burrito person more than tacos. I'd say my favorite— I don't know, I wouldn't call it a taco shop, but, uh, El Pescador in La Jolla is one of my favorite places to get tacos. Taco shop-wise, Bahia in La Jolla, uh, lobster tacos, shrimp tacos there are as good as they come. Um, so those are probably my My top 2 picks.

All right, Chad, how about you?

A couple of years ago, my friends put me on to Tacos El Gordo. The first location, I think, is in El Cajon. They just opened up one maybe, you know, not so recently in, in downtown.

But the al pastor tacos to that part, legendary, has its own line just for the al pastor. Yeah, that's how you know. That's how you know what to order there. That's for sure.

What's your favorite tacos, Neal Bloom?

Ooh.

So I started this pod as a burrito guy, by the way. And I was a California burrito from Sombreros, like, like old school dirty taco shop as well too. But through the, through, I think more than 300 different taco places mentioned now, uh, I'm really into trying every taco at City Tacos these days. I just want to eat the entire menu, you know? Um, So that's my place of choice these days is City Tacos. There's one in Encinitas that I end up at a lot, but they're all around. They, they seem to be everywhere that there's a taco stand. Like there's also a City Tacos nearby too, smartly, because there's always a line at the taco stand.

Right.

So, uh, they're smart about that. Uh, well guys, this has been great to have you on. Uh, I remember my first inclination that Grayson was building something new was that he posted on Instagram. Anyone know someone in their 20s that wants to go build an AI, uh, service-based business? And look what you've done. And look what Sequoia has noticed. And look where you guys are on your way.

So, uh, God's old now though. He just turned, I think, 31 or 32.

Oh, well, when you started it, when you started it.

I, I was 20, I was 29 when we started it.

But he was right at the tail end. Somebody hungry and motivated and that knows where technology's at.

So. Well, we really appreciate it. We're excited to just learn from you, from boots on the ground, where you are seeing, right, where businesses are changing. So thanks for joining us here, and thanks for what you're doing for making everyone more efficient so that we can serve our community. Thanks, guys.

Thanks, Neal Bloom. Cheers. ¡Gordo! The taco stand in puesto, lucha libre en sombrero. Gotta shout out Tecos, angels by the ocean, watch the sunset. La Jolla shows entrepreneurs trying to get funded when San Diego Startup Week is popping in the summer. Oceanside to Tijuana, SD be full of hustlers. From couch surfing to penthouses right on the beach. Tech leaders who believe stars are within in reach. We interviewin' founders who be shootin' for the moon. #SDTacos with your host Neal Bloom. Startups get the party, startups get the party started. Startups get the party, startups get the party started. Startups get the party, startups get the party started. You want tech in tacos, tacos in tech, you know we got it, hey! Startups get the party, startups get the party started. Get the party started. Startups get the party. Startups get the party started. Startups get the party. Startups get the party started. You want tech and tacos, tacos and tech, you know we got it, hey!