#266 Len Pagon — Robots & Pencils

Len Pagon [00:00:00]:
We have now the first one done in like 2 weeks. We went from just a rough idea to a polished idea to actually implementing it for the first time in like a 2 or 3 week timeframe. And it created all this other learning. So my point with the AI sort of enabling what it means to be human is I think we're all creators and builders. You know, now it strips away the drudgery. So now I can sort of, you know, refine an idea in a, in a, in a sort of collaborate with the tools where it's coming back with better ideas. And it's just, it's just really incredible that way.

 

Jeffrey Stern [00:00:30]:
Welcome to the Lay of the Land podcast, where we are exploring what people are building in Cleveland and throughout Northeast Ohio. I am your host, Geoffrey Stern, and today I had the real pleasure of speaking with Len Pagan, CEO of Robots and Pencils. Len has spent his career building and investing in technology businesses. He founded Brulon in his early 20s, grew it through multiple waves of technological change, later merged it with Rosetta, and then spent the next chapter investing through NextSpark, his family office. Nearly 2 decades after leaving the CEO seat, Len has found himself back in it at Robots and Pencils to take full advantage of the opportunity that AI presents. In our conversation, we explore that return to founder mode, why he needed to wait for something sufficiently compelling to get him to make that leap, rebuilding Robots and Pencils as an AI-first company, why speed and constraints can fundamentally change how people work, and how AI is reshaping consulting, knowledge work, and organizational learning. We talk about leadership the second time around, Earned wisdom, and the renewed sense of purpose Len has found in building alongside a team again. Please enjoy this insightful conversation with Len Bacon.

 

Jeffrey Stern [00:01:42]:
Lay of the Land is brought to you and is proudly sponsored by Serity Partners. As a wealth management firm, Serity Partners shares Lay of the Land's same dedication to serving local business owners, and the Serity Partners Cleveland team understands the challenges that entrepreneurs and founders face here in Cleveland, Northeast Ohio, and beyond. Wealth comes with complexity and increased demands on time and resources. It is easy to become overwhelmed. Serenity Partners clients benefit from a unified team of local specialists who coordinate across both business and personal needs. With Serenity Partners' commitment to transparency and putting clients' needs first, complexity can become clarity. To learn more, please visit serenitypartners.com or call 216- 646-4266 today. Seriti Partners, proud to be recognized as one of the top financial advisory firms in the country.

 

Jeffrey Stern [00:02:37]:
All right, so Len, when we first met, you told me that after nearly 20 years away from running a company, that you have found yourself back in the CEO seat at Robots and Pencils, and maybe not entirely by choice and perhaps with some mixed feelings about it, but then with some time feeling it was a blessing of sorts and working on what you feel you're meant to be doing, and all of it coinciding with the craziness that's going on with AI. And I thought it would be a fun place to start really at that intersection of those two and ask, why jump back into the arena at this point? What did you see in yourself, in robots and pencils, in AI that convinced you this was the moment to get back into

 

Len Pagon [00:03:32]:
it? That's an interesting question. 15 months ago, I was asking myself the same question. And I had built a business called Nexmark and then Brillant for 19 years, and then I sold it in the summer of July 2008 before the Great Recession. And then I left in 2009 after the Great Recession happened. And so I thought maybe someday I'll run another business. Then I kind of founded our family office and we were investing in businesses and was not retired. I was actively working with our portfolio businesses and on a bunch of boards. And it's funny because one of the boards I was on was a company called Stone— well, one of the the board meetings I participated in was a company called Stonebriar, and it was run by a Texan.

 

Len Pagon [00:04:22]:
He had this amazing saying that if it's not hell yes, it's hell no. That is a very Texas kind of saying, but it really resonated with me. We had all these investments, and a lot of times, you kind of get excited about it, but you're not really super excited. I would keep coming back to this, if it's not hell yes, it's hell no. I was faced with a decision. We had bought Robots and Pencils, my family office did, about 8 years prior to me resuming becoming a CEO again. And I've been very intentional about my decisions. I think about them.

 

Len Pagon [00:05:01]:
And again, if it's not hell yes, it's hell no. And so the CEO that was leading the company wasn't doing a very good job. I was actually in the The mode where we were going to do his annual review, he was a Stanford MBA, built another very successful entrepreneur, built another services business from $500,000 to $160 million, but things were not going well at Robots and Pencils at all. And so I was going to do his annual review. I started talking to his senior executives and it became clear pretty quick that this was not going to be a tough review anymore. It was, I have to replace him. And so as I looked at who potentially could run the business on an interim basis. There was really no real good solutions, and there was no obvious even people for me to put in an interim basis.

 

Len Pagon [00:05:48]:
And so I thought deeply, like, okay, there was part of me that was excited about maybe running a business as a change of pace, certainly very excited about the AI opportunity. But after all of that consideration, there was really only a third of me that was excited about it and 2/3 of me that was like, I don't think I want to do this. Like, I like my flexibility in my life. What's that, Jeffrey?

 

Jeffrey Stern [00:06:14]:
That doesn't sound like a hell yes yet.

 

Len Pagon [00:06:16]:
No, it was mostly a no. But I was kind of— I've been for a long time, I even go back the last couple of years prior to that, there was part of me that was feeling like something was missing. that I wanted to be doing something different and more. And, trying to think that— and, I was also feeling that I have all this amazing experience and success in the past and how can I leverage that? I felt like maybe this has all been just a dress rehearsal for a moment. Again, not thinking that it was robots and pencils per se. So, long story short, I I fired the prior CEO and I decided to be a new CEO. I was very curious to how it was going to feel. Am I going to be thinking that time is all of a sudden moving slow? And how is it going to be feeling really with getting dirt under my fingernails? There was a lot of heavy lifting.

 

Len Pagon [00:07:16]:
It was a turnaround. The company was actually in a bit of distress. We had no sales pipeline or go-to-market motion. We really didn't have a committed strategy. We had a lot of great people that were there at the company still, but there was no real strong leadership team. There was a lot of heavy lifting that needed to be done. Right away, I knew as soon as I now was in the job, even leading up to it where it was now go time, I got to fire the guy. I was even talking to the leadership before I terminated him.

 

Len Pagon [00:07:49]:
to prepare for that. So I can't just terminate him and then say, hello, everybody. Okay, let's figure things out. There was a communication plan. We had an all-hands meeting scheduled. I wanted to know, we had a lot of turnover. The company morale was really bad, again, despite a lot of amazing people that were there, about almost 100, about 90 people that were still there. And so I got a list of who's at risk at the whole company, and it was 50% red the day before.

 

Len Pagon [00:08:20]:
I wanted to know who I was going to personally reach out to and have a very good communication plan. So, even before that week, it was actually Memorial Day of last year, that weekend where I was having to prepare for it. But, as soon as I was in the seat, I've been in founder mode and I've been in founder mode ever since. And, as I told you before, I feel like I'm exercising all of my skills and talents. The analogy I tell people is now I'm a grandparent and it's the difference— your brain is different. Your brain gets wired different as a 60-year-old. And it's the difference between having kids and being a grandparent. You just see the children very differently.

 

Len Pagon [00:09:02]:
The first time around, it's a very enjoyable experience, but you have stresses like, I don't know about this, and are they hungry? And are they— they're just unhappy or they're— you're just in it. And now, as a grandparent, I just see them as my grandchildren as just this little constant parade of all these miracles happening. It's just amazing. And just your brain is a very different brain. And so similarly, me as a CEO now, I just have the benefit of wisdom and a whole bunch of experience. And you have this sort of what Arthur Brooks calls crystallized intelligence where you just sort of know stuff. And so on a Tuesday, I had the discussion with the prior CEO. So I scheduled an all-hands meeting.

 

Len Pagon [00:09:45]:
On that all-hands meeting, I kind of disputed some of the concerns, dispelled some of the very significant concerns that existed. And then I just said, we are an AI-first company starting today. Everybody has to learn how to use these tools. I don't care if you're the accountant, accounts receivable clerk, or a consultant that's doing work on behalf of our client. We all have to figure out how to be more productive. And I said, at the same time, I don't know what it means to be an AI-first company. I only sort of know what that means, but we're all going to figure it out together. We're in this amazing time.

 

Len Pagon [00:10:19]:
This is not just a generational opportunity. This is a once-in-humanity opportunity. This is way bigger than the internet or mobile or the advent of the PC. We're never going to go through this moment again. And I said, we can be in the arena figuring this out, and either we're on the bus, on the AI bus, you know, along for the ride together, or we're going to get run over by this bus and be disrupted. And I said, I'd rather be on the bus. And so it's been an amazing ride. And, um, yeah, and, and I— so again, I was— it wasn't like an evolution.

 

Len Pagon [00:10:53]:
It was like I jumped in and all of a sudden wasn't sure how it was going to feel, but it was— it's been an amazing experience.

 

Jeffrey Stern [00:11:00]:
What is the, the story of Robot and Pencils?

 

Len Pagon [00:11:04]:
Where the name came from or where did the— just the founding or—

 

Jeffrey Stern [00:11:07]:
Yeah, the origins of it.

 

Len Pagon [00:11:09]:
Well, so the— first of all, the origin of the name is, um, it was inspired actually right after, uh, Steve Jobs introduced the iPhone. So that was the inspiration. I didn't found the company, but, um, there's a husband and wife that founded it in Calgary, Canada. Very, uh, MJ Sikorsky is like really, really smart. technologist, just super, super bright. And his wife, Camille, who was very creative, she was kind of an interior designer. She just had a real flair for design. And so they, in the wake of or in the advent of the iPhone, they were inspired by a speech that was given by a polymath and Cambridge grad in 1959 called C.P.

 

Len Pagon [00:11:54]:
Snow. And so he As you might imagine, in Cambridge, there's a lot of philosophers and there's also a lot of scientists. And so his speech was really about that all of the world's— the biggest barrier to solving the world's greatest problems was the divide between the sciences and the humanities, that they were having this big academic debate. And if we could just get them to kind of unite and come together that we could actually solve the world's biggest problems. And so, the inspiration for Robots and Pencils was that's the integration of the scientists and the sciences and the humanities, the engineers and the designers, the robots and the pencils. And so, what their theory was, and which is it's about us coming together to kind of left brain, right brain, however you want to think of it, to be able to do amazing work. And, it's interesting. So, we even thought that our engineers were called robots.

 

Len Pagon [00:12:57]:
They referred to them internally as robots. We're really good at the user experience design. They have amazing designers. And so, they refer to themselves as the pencils. And then, you have people that are project managers, product managers that bring those 2 disciplines and crafts together. And there, they actually would be called the ampersands. And so, that's where the name came about. That's where the company came about.

 

Len Pagon [00:13:17]:
But then, we got involved. The husband and wife, The husband wanted to continue to scale the business. The wife actually just wanted sort of a family lifestyle business. And so, we got involved and we bought majority of the business about 9 years ago and became the partner of primarily the husband who was the CEO at the time, MJ. And, the wife kind of went back home with the kids and the girls and really focused on that. And so, I've been the chairman. I've been involved with the business since we acquired it 9 years ago. And again, I've been very actively involved during that time.

 

Jeffrey Stern [00:13:58]:
What are the problems that Robots and Pencils is trying to solve?

 

Len Pagon [00:14:02]:
Well, the overarching thing is this is AI. So generative AI, which is where you'd use AI like the models to generate images and generate stuff or code. And then you have agentic AI. And so what we do is we help partner with our clients to apply that to help improve their businesses. And so what our clients are coming to us for is they want a partner that's going to be able to help them identify what the use cases are, help them bring in the ready-made expertise that we can architect and implement those solutions.

 

Jeffrey Stern [00:14:41]:
So what have you learned about what it actually means to build an AI-native company? You mentioned coming in, it's sort of a, we're going to build this as we do it and figure it out as we go. But what have you learned about what it means to actually build an AI company? And then on the flip side of that, what you're working with is helping other companies that want to do AI. What have you learned about just what it takes to actually get started and where the right place to start is, both internally and externally?

 

Len Pagon [00:15:15]:
Well, come back. So there, yeah, I could talk longer than we have the podcast about all the things that I've learned. That's only 2 or 2+ questions. Let me think about this. So first of all, we had a very— our people were very talented. and very technology forward. So they were already doing, even though I said AI first, like I said, it was a commitment that we are AI first in everything that we do. We got to figure that out.

 

Len Pagon [00:15:42]:
However, the company had been doing machine learning. We had been doing artificial intelligence in kind of the old way. We were building bots. We built this very good chatbot for Ingram Micro as a customer service bot. It was very expensive, not very good by today's standards. I don't know, that was like 5 or 6 years ago. It took us a year to build the thing. We had a very strong technical capability and design capability.

 

Len Pagon [00:16:13]:
The first learning is that, and kind of, I guess, answering maybe your second question first, is I just said all I was focused on is adoption, and I want you to be using these tools every day. Initially, people are using ChatGPT and they're just using through the chat, they're doing research. Then people are using Claude Code and they're starting to build things. Some people are already building things, whether it's Claude or Codex. It wasn't Codex at the time, but through Cursor. And then we started building things. And so things, I was very Very pleasantly surprised with how fast we were moving. And I was very pleasantly surprised.

 

Len Pagon [00:16:59]:
One of the things that I kept harping on over and over is I said, we're all figuring this out together. And it's okay to make mistakes. Like, we just have to learn. And if you make mistakes, as long as we learn from those mistakes, it's okay. Like, I was giving people permission to fail and permission to experiment. In retrospect, as I talked to our people, they're like, you were like a breath of fresh air. Like, we never, you know, everybody was kind of like— the prior CEO did not give him the permission to fail. He was very authoritarian.

 

Len Pagon [00:17:32]:
Yep. And so it was a breath of fresh air just to give him the permission. I think us being very committed strategically to say, this is where we want to be, one of the leading agentic and GenAI providers, solution providers, And we're going to use AI to reinvent what a services firm is, how we deliver our work powered by AI. So not only is AI kind of the value proposition, our core competency, we're using AI in every aspect of our business to kind of reinvent how work gets done, how we service our clients, how to speed things up. And so I just was blown away by how it was kind of like bringing water to thirsty people. And they just, just went after it.

 

Jeffrey Stern [00:18:18]:
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Len Pagon [00:19:22]:
The thing, the next big learning is I learned that there's a huge power in constraints. And I think this is something that, and so that's, I think the best way for people to get started is for the CEOs and their leadership team to just start using the tools. I think it's very, they need to be very clear on like, And this is really important. Either we're going to get disrupted by it, then this is an existential threat. Also, this is a huge opportunity for everybody to be using these tools to full power. So, the second thing that we kind of created as we were doing now pitches for clients and we started getting a sales pipeline is I said, we don't want to just sort of give good PowerPoint. We don't give what we kind of jokingly say is give good deck or death by PowerPoint. We don't want to just say, Here's what we're going to do, and we do a PowerPoint presentation.

 

Len Pagon [00:20:06]:
What I wanted to do, as we talked about with our leadership team, is actually show them you can build amazing things in a very short amount of time. And so we started really quick, built it, completely changed our go-to-market approach where our clients would say, here's the thing we want to do. And we'd say, give us your documents, give us your artifacts. We started transcribing in every conversation we had with our clients. We're now using Fireflies. We would get the transcriptions from those conversations. We could use those as context into the AI tool, into the AI agents, again, like Claude, and actually build something. And so in a very short amount of time, in a week or 2, we could actually show the client what they wanted.

 

Len Pagon [00:20:47]:
It wasn't final production by any means, but it also wasn't a throwaway prototype. It could be the beginning of something. And so we ultimately called that a Milestone Zero. Or an M0 for short. And so we used AI to change the way that we did the business process, the business development process. And now the conversation with a client wasn't like, okay, here's PowerPoint, here's how long it takes, here's the team structure, here's our methodology. We are actually saying, this is what we heard. And now you're having a very lean forward moment with a client where they're talking about, well, could it do this and could it do that? And that's kind of what we're thinking.

 

Len Pagon [00:21:25]:
How did you guys know this? And so, it was just like the beginning of something. And we would just say, if you want to hire us to continue to build this thing, here's kind of what that involves. And so, the second part of that though, as we started doing these M0s, we would do very aggressive delivery in a very compressed timeframe. And so, what I realized is we would say, here's the things that we want you to do. our internal team to do this for the client, but you got a week to do it. And they had never done it that way before. It was like a very challenging kind of thing. And they would say, they would just sort of be dumbfounded like, we can't do that.

 

Len Pagon [00:22:04]:
Like, we've never done that. And how do you do that? And so, what we realized by that constraint, that forced a whole bunch of behavior change in a very short amount of time. And so, in a week— That's awesome. By the first day, they're like, this is impossible. The second day, they're saying, Okay, well, maybe we can kind of do it. By the third day, they're actually adding other ideas and scope into the M0 because now they can see that there's so much more that they can do. And so, it was that compression and that constraint that created a whole bunch of rapid behavior change. And so, that was a huge learning for me that the M0s were not just a great advantage to win work with clients, but it became a tool that we try to do everything by having constraints and really tight timelines, it became practice for our teams to really learn how to work differently.

 

Len Pagon [00:22:57]:
I'll pause there. I got more. Again, I could keep going about lots of learnings. I'll give you one other one.

 

Jeffrey Stern [00:23:08]:
Sure.

 

Len Pagon [00:23:09]:
As I use the tools, one of the things that is amazing, and I'm doing Claude work and I'm doing a little bit of stuff with Claude Code, I see our CFO doing amazing things and our chief marketing officer doing amazing things with the tools and the velocity that they're able to generate. But for me, what I realize is I'm now able to create, I become more human by using these tools because it strips away the drudgery. I can sort of say, I'll have an idea like this MZero example that I just gave you. I had this inkling of an idea. And initially, we called it the perfect pitch. And like, I don't— the perfect pitch seems win or lose. Like, I want it to be the beginning, the inception of something. And so first, I'm kind of figuring out the naming of it, and then I'm figuring out like the process and how to package it.

 

Len Pagon [00:23:56]:
So I'm collaborating with the tools. And instead of it taking me in, you know, in my past life when I had the business, that concept might have taken me weeks or months to develop that idea with some collaboration with our people and our designers, and they'd come back and You'd sort of evolve the idea and collaborate. Here, I was able to sort of have the nugget idea, like have it really kind of like a polished nugget. It was still a bit raw. I was able to present that to our client, our internal executive team. And then a week later, one of our guys was like running with it and like doing it, doing the first MZero for one of our clients. And so we have now the first one done in like 2 weeks. We went from just a rough idea to a polished idea to actually implementing it for the first time.

 

Len Pagon [00:24:41]:
in like a 2 or 3 week timeframe and it created all this other learning. So my point with the AI sort of enabling what it means to be human is I think we're all creators and builders and now it strips away the drudgery. So now I can sort of refine an idea and sort of collaborate with the tools where it's coming back with better ideas and it's just really incredible that way.

 

Jeffrey Stern [00:25:05]:
Yeah, a lot of threads to pull on here. We can riff just on AI for a bit. I think one idea that you introduced there that is one of the more interesting things I've seen in practice is AI beginning to capture knowledge that historically really just was not written down anywhere, and it's lived in the heads of people like yourself or the best salesperson on the team or the person that had been doing the job for 20 years. I mean, how important is that? Does— how are you thinking about like how AI fundamentally changes a company's ability to turn that kind of earned wisdom and kind of individual judgment and experience into institutional knowledge? And where do you feel like the value accrues inside of a company?

 

Len Pagon [00:26:01]:
Well, so we're, I think that's what you just are talking about is I think probably a newer insight for me and how I think about, for us internally, our own internal business, but also how we can do things on behalf of our clients. And so as you, have you, do you use Claude or do you use Cursor or any of those tools? Are you—

 

Jeffrey Stern [00:26:21]:
Oh yeah.

 

Len Pagon [00:26:21]:
Have you been building skills or plugins or agents or any of that stuff?

 

Jeffrey Stern [00:26:25]:
Absolutely.

 

Len Pagon [00:26:26]:
Oh, so now, so you know what I mean. So I'll give you 2 examples. where I think it takes a little bit for the kind of a mental model shift. And so the first example, about, I want to say, 3 or 4 months ago, we have all these spreadsheets in our finance function, like every other organization, and they're very cumbersome because we're doing business in Colombia and Canada and Eastern Europe and the US, and we have all these— we're a global delivery organization, and so there's very complicated balance reconciliation and you got different currencies. And so all these accounting processes are very complicated. And so I kind of challenged our CFO and he had an analyst, it's Jake Bruschino that works with him. And I said, like, why don't you just use these AI tools to streamline a bunch of stuff? And in literally, I don't know, 2 weeks from him going, and they're not technical, they're very smart, but not very technical. And in 2 weeks, like, they blew me away.

 

Len Pagon [00:27:24]:
They started building stuff. It wasn't spreadsheets anymore. They started building these tools. Like we have this engagement setup form that does the profitability, we set up the projects, but they now had a, instead of it being a spreadsheet, it was like this tool. We have this weekly flash report where we get a spreadsheet with all, it's like a very complicated spreadsheet with all these tabs and stuff. And so they built something that came outta Claude Code in HTML files that's dynamic and does all this stuff. And so that was kind of the first step and, oh, now you're using the tools, that's really cool. Well, I was meeting with him To answer, kind of give you an example, a few weeks ago, and he now showed me how he's automated like 80%, that those 2 people automated like 80% of the accounting function.

 

Len Pagon [00:28:05]:
So it does the bank reconciliations, it does the currency stuff, it does some payroll stuff, it does the final ledger and all these different kinds of the accounts receivable. There's a whole step of a whole bunch of processes he does on a weekly and monthly basis. And he's got them mostly automated with AI. And he says, in about another 2 months, I'll have almost 100%, you know, we'll be able to run an organization 5 times larger with almost the same size finance and accounting group. And so as we're talking about it, you know, I've been pushing on him to get more quality MD&A management discussion and analysis is where the action is. And I said, now that you get that automated, I said, I have a new challenge for you. Again, this is only in a few months timeframe that he's gotten to this point.

 

Jeffrey Stern [00:28:49]:
Yeah, yeah.

 

Len Pagon [00:28:50]:
And I said, imagine you have this, now you have this reporting and you have all this stuff and it's all automated, it's all, you know, it's happening really quick. I said, imagine the conversation you're gonna have with one of our other executives that runs a line of business, or imagine the conversation you would be sitting down with this, armed with this information with one of our middle managers. I said, now I want your MD&A to be really, really high fidelity. The reports are just the reports. Somebody's gotta analyze those reports and figure out, well, I got a margin trend, I got a pricing problem, I got a whole bunch of free work that I'm giving away for this client. So you're managing different problems on a weekly or monthly basis, and then you take actions. What I want you to do now is build the skills that are actually going to shine the light on what these managers should be doing. Where are the biggest issues? What are the biggest actions that they need to be taking? Instead of them looking at a 28-page monthly report, actually give them a 2-page or 1-page Here's the actions that I need to take so that now he's building skills that I'll actually, you know, I'll put it, I'll say gives the mDNA on steroids for the, that's relevant for the practice leader, relevant for the vertical leader that's running a P&L, relevant for somebody that's, you know, whatever their part of the business is that it's relevant for that part of the business.

 

Len Pagon [00:30:09]:
So, that's the first example. The second thing we're doing is we have this So as you're building AI solutions, we are, as all of us are doing, are kind of on this endless fire hose, drinking out of a fire hose of learning. We are too. We're way ahead of our clients, but we're still drinking from a fire hose. And so we're constantly working on our AI proficiency. And so we want to get somebody that's a 1x developer to be a builder. We call them AI builders, to be a 10x or initially a 5x. And then there are 10x.

 

Len Pagon [00:30:44]:
What you find is that there's huge opportunities to improve the quality of the prompts that they're using, the quality of the context they're doing to set the stage for the prompts, what's called context engineering. There's huge opportunities to reduce the cost of producing the output. We have massive token expenses now, and so we're trying to get better Output for lower cost of the tokens. We want more yield from the tokens. And so we know now that if you have poorly written prompts, they're going to be inefficient. They're going to be preloading. If you're using cache, a cached prompt, it's 90% lower cost than if it's uncached. So if it's in memory, resident in memory already with a model, it's 90%, you know, it's only 10% of the cost.

 

Len Pagon [00:31:35]:
if people are using the wrong model, like we had some guy that was building 90% of his stuff in Opus, which is the second most expensive, and even some of it in Fable, which is the most expensive model, that if they're using that as default for 90% of their work, their load, well, why don't you start with Sonnet? They should be using Sonnet for probably about 80% or 90%. If it's not getting good quality, then you can go up to the other models. And so, What we're doing is instrumenting kind of a cockpit for our builders so that they can see their tokens on a daily basis, that they can see what their yield is, how many commits and PRs that they have, or PRDs and stories that— it's the velocity of their work that we're tracking. And then the first step was to get them the data. Now, what we're doing is actually building coaches on top of it. So, it's not just saying your tokens are this, We're saying, you know what, you're using cache a very limited amount of time. You can be using caching and here's how you use the caching. So now we're building coaching on top of the telemetry.

 

Len Pagon [00:32:38]:
So it's about giving them the behavior change and the insight. And we're doing that with skills that actually can measure the proficiency, can look at the quality of their prompts that they're writing. So it gives them advice in real time for the stuff that they're doing. And so we're in the process of building those things. Claude doesn't come out of the box. So you're really in it. There's lots of room for improvement and being way more efficient. So those are 2 examples how we're building that into everything we're doing.

 

Len Pagon [00:33:03]:
We're doing the same thing with recruiting and interviews. We're doing it with the same, like every aspect of our business, we're building that intelligence and adding skills on top of that, where we have very senior people with very experienced domain expertise. And then now they're writing the skills that can actually get really good at the, again, sort of qualification, And even the interviews that we do for all the people that we're hiring, we record the transcripts and then we can get feedback to the interviews and then give you kind of a ranking of how people are doing in terms of where we should be hiring or not. We're doing that all over the place.

 

Jeffrey Stern [00:33:39]:
Yeah. I mean, there's sort of this hedonic treadmill of AI where, I mean, the pace and velocity of its advancement is so fast. that you sort of lose track of how much more capable it is at given points in time, and you quickly kind of baseline back to, you know, you're just sort of taking for granted, but that's just because how fast it's moving. I mean, how are you orienting around the future and like what, just who is, what are the companies that you're working with that are actually able to adopt this and keep pace with the firehose that you guys are drinking from? You know, just where is this actually— where's the rubber meeting the road for a lot of these companies that you're working with? And what is like actually fundamentally changing within these companies?

 

Len Pagon [00:34:37]:
Well, I'm not sure that this is your question, but this is an interpretation of your question. I have realized that the inner loop— so first of all, if I back up, I see us, our strategic value is a couple of things. We see ourselves as the speedboat of Navy SEALs that can come and work with our clients and deliver very rapid and high impact. And we can do that with a small team, what we call pods, again, in a speedboat. So we land on the beach, at our client's beach. And then we can kind of sort things out and really rapidly sort of make impact. So we see our value proposition as making the business impact, but doing it in increasingly compressed timeframes, what we call live in 45. And so in 45 days, we're implementing something in production.

 

Len Pagon [00:35:28]:
It may not be perfect, but it's not a prototype. We're implementing something that you can try and experiment with. So we see that underneath that, so velocity is our value proposition. Again, you got to make the business impact, And we think we're a disruptor to the large GSIs. We think there's a huge market opportunity, the large GSIs being Accenture, Deloitte, EY, Cognizant. And they're the aircraft carrier that if you need a mission that you've got to deploy hundreds or thousands of consultants, they're really good at that mission. If you're a large Fortune 500 company that's implementing SAP or a new warehouse management system, or you're doing an AT&T migrating 65,000 servers over to AWS, They're good for that mission. We don't have the scale for that.

 

Len Pagon [00:36:14]:
But we're really good, powered by AI, to make rapid impact. What I've realized recently, kind of going onto this skills question a little bit, is the inner loop is the velocity to learn and the velocity of behavior change. And so what we're trying to do is accelerate. It's not just the mental model of how people learn is you do some tutorials and you do maybe some mentoring and tutoring, that's okay and that's helpful, but that's a little bit of the sprinkles on the icing of the cake. The cake is learning by doing. It's the work that you're doing every day and working with other smart people side by side that you can see what they're doing. And you get a certain amount of velocity from an individual contributor building their own skills. You get way more velocity by a whole bunch of people.

 

Len Pagon [00:37:03]:
Now I can share skills that are already prebuilt. But I just need to know what those skills are, and then I can refine that skill. I can further harden it to make it better and better over time. That's how you unlock a whole bunch more velocity and you continue to what we call these flywheels where we're getting continuous improvement all the time, that we're constantly adding new skills, we're refining those skills, the models are getting better. Our people are getting better at using the tools and leveraging more of the work to digital workers, to the AI. So all of that is creating acceleration. Again, the constraint is one's ability to learn how to use those and what's there, and also to change their behavior. You got to change the behavior.

 

Len Pagon [00:37:46]:
If you're not doing that— that's one aspect of what you were saying. Now, you were saying what— I think there was another question about our clients, but we'll go back to that.

 

Jeffrey Stern [00:37:56]:
Yeah, it is an open question. tenets of it are where the rubber meets the road, right? So in companies I'm working with that are implementing AI, it sort of becomes transformative when it stops just producing insight and actually starts doing some of the work. And so I think there's this component of AI as intelligence and AI as execution. But then it's also, it's not just about its capability. That's sort of necessary but not sufficient. And like how adoption really only takes off when it appears within the tools that people want to use and are already using because changing behavior is really hard. So just what I'm curious about is how you get the right buy-in for adoption?

 

Len Pagon [00:38:56]:
Well, my opinion from my own personal experience is it starts with the CEO and the leadership. I think you have to say, this is really important. Actually, more than important, this is a strategic imperative and maybe even the most important, depending on your business. Some businesses are a little bit more resistant and have more time. If you have a Yeah, I don't know, a utility. If you're a utility company, you have the wires and the transformers and the power gen and all that stuff. So, it becomes more important over time how you leverage that with your workforce, whether it's customer service or deploying field service and getting more efficient with field service, or getting better telemetry and insight from the equipment that you have out in the field. Like, how can I do preventative maintenance so I have less outages? That kind of stuff.

 

Len Pagon [00:39:49]:
But again, utilities, you got big— AI is not going to replace power generation and transformers. You have all that. So that's a resistant thing. If you're an insurance company or you have a lot of white-collar workers or a lot of office workers like that, or in our business, a consulting company where you have a lot of knowledge work being done, those things are very ripe for being disrupted. And so, I think it starts with the leadership to say, this is really important, and then getting a more than just— and then first, you're focused on adoption. So, the first phase is, like, I started looking at tokens for everybody, and I wasn't looking at it for, like, there's a whole bunch of backlash with token maxing and all that kind of stuff. I didn't care. The first phase is I just want everybody to be generating tokens, or I wanted to see that they were, because I knew that they were using the tools.

 

Len Pagon [00:40:40]:
And so, that's sort of the first phase. The second phase is then you want to work on the proficiency. And so, I want insight to how they're using the tools and getting more proficient. The third phase is now I want to see that stuff being embedded. I want them to be doing— there's a level 0 to a level 5. And so, we measure the proficiency of how they're using the tools. And I want great proficiency like my CFO to be doing that at all levels of the organization so that they're encapsulating their knowledge work into the organization and then even building and automating a lot of stuff with the agents to be doing the digital work. So, it's kind of a progression or fluency progression in terms of your lifecycle of how you're actually using that organizationally.

 

Len Pagon [00:41:22]:
It's good to be happening in pockets where you really— and you can get a lot of benefit of that, especially if those pockets are doing a lot of high value, if there's a lot of great use cases there. However, the big opportunity and big unlock is as you start to do it organizationally and then start to figure out how to do the work differently, not just automate the work that you're doing already using the AI, but how do you start to really transform the organization? How do you start to create new products and services wrapped around your products? Those are the things that are even— it's not just automating what you're already doing, it's how do I do things that I couldn't do before? And that's actually a great place to start often because you may not have the dependencies that a lot of organizations have. If you have bad data quality, if you have tech debt, if you have, I don't know, I'll just say onerous policies and guardrails that prevent you, we see some organizations that you just can't do anything because they're afraid of it. You got to have some modern policies and guardrails so that it unlocks people's proficiency to be able to use these tools. So again, AI does a lot of amazing things, but it won't solve data quality issues. It won't solve cultural things. It won't solve policy, onerous policies. It won't solve tech debt.

 

Len Pagon [00:42:37]:
If I got these crappy old systems, you gotta sometimes modernize that or insulate it so that those things don't become huge dependencies. You'll only be able to move as fast as those constraints allow you to.

 

Jeffrey Stern [00:42:51]:
When you eventually look back on this Robots and Pencils chapter, what would have to be true for you to consider it a great success? Like, what do you feel is your North Star in this?

 

Len Pagon [00:43:05]:
So I'll give you 2 answers to that. So on one hand, I've already Realized— for me, it's almost like a purpose. I've realized a new purpose. What I'm doing today, it may not be running Robots and Pencils, but I will be doing more or less what I'm doing today for a long time and leading something. I was meant to be building businesses and leading people. Again, I was doing that with my family office on a smaller scale, but this is how I can have the most impact or leverage. and do good. I just love what I'm doing.

 

Len Pagon [00:43:47]:
And so from that standpoint, it's already a checkmark. This has been a great validation. And I was, again, as I told you at the beginning of this, it was almost a hell no that turned into a hell yes. And so that's been a very pleasant surprise. I didn't expect that. In terms of my aspirations for robots and pencils, I think we have the opportunity to be a global leader in what we do over the next 5 years. We are already delivering for some of the biggest companies in the world. I think it's a huge opportunity.

 

Len Pagon [00:44:21]:
I think we're competing very well against the large GSIs. Our clients are very fatigued at working with those GSIs. They have to support a lot of overhead. They have to have a different sort of delivery model with one partner with a whole bunch of junior people. They're wanting companies that can be very nimble, very hungry, and deliver a lot of business impact. And I think that our opportunity is very big. Another great surprise for me, again, going back to the 60-year-old brain crystallized intelligence, is we're able to move so much faster than I was when I built my business before. We've accomplished more in the last 15 months.

 

Len Pagon [00:45:02]:
It would've taken me 5 years of what we did before, just because I rebuilt my new executive team in about 2 weeks. That took me years and a lot of mistakes and bad hires the first time around. I made a lot of hiring mistakes. This time, I knew the people. We already had the relationships. We already worked together before, many of them. And so, we were able to just click back into place and make very, very rapid progress. And people just sort of— and it's fun.

 

Len Pagon [00:45:30]:
People are just getting stuff done and improving the business every day. Yeah. And so, I think, yeah, we have a big opportunity to be a global leader. We were handpicked from AWS in a bunch of different programs. When I started as the CEO, we were not even an AWS partner. I asked our people, I said, how fast can we become a partner? I challenged them. And then, 5 weeks later, we are an AWS partner, but the lowest-level partner, one of 150,000 partners in the globe. And now we're in 4 different programs where we are one of, in one program, we're one of like 15 in the globe.

 

Len Pagon [00:46:04]:
AWS handpicked us. Another partnership, we're one of 20 in this BVR program that they just announced this past June. We're one of 25 companies that were selected globally, and we're doing work side by side now with AWS. We have an MSA with them, and we're doing side-by-side work with them in Meta, doing this incredible work. With Barclays, with leading organizations doing really, really cool, really tough, exciting work with them. And so, I think we have the opportunity to just continue to do that with the amazing people that we have on board. And, what's exciting is the GSIs, which have a lot of very talented people, we're able to handpick and cherry-pick the best, most talented people because they want to come to work for us, they want to be part of this really special thing that we have going on.

 

Jeffrey Stern [00:46:51]:
To revisit that crystallized knowledge concept you introduced and the, the grandparent analogy, I mean, if you just kind of take what you just spoke to and look back at the arc of your career, you know, you've gone from founding Brûlant in your early 20s, and then living through multiple successive technological waves, And I'm very curious what you feel you learned from those that is applicable today, and then what just feels completely new about this AI wave relative to building in those eras.

 

Len Pagon [00:47:33]:
So what's new and what resembles or similar rhymes a little bit with the past you're saying?

 

Jeffrey Stern [00:47:39]:
Yeah. And where does that allow for you to leverage that crystallized knowledge that you have versus Where do you feel fully humbled today by what is happening?

 

Len Pagon [00:47:49]:
Yeah. So on the part that rhymes or the similarities or where the experience is relevant, I mean, I had my former company that I ran for 19 years, I had to reinvent it 3 times. So we initially started just doing Macintosh development. We were good at the apps and good at the user experience in 1989. And then client-server became a thing in the early '90s. Partnered with Microsoft and Sybase and Oracle. And so, we started building client-server applications across Microsoft and the Macintosh. Then, the web came out, and so we became initially a web development company.

 

Len Pagon [00:48:27]:
And then, in the late '90s, we started hiring people from Accenture, and we became similar to a company called Sapient. So, we were an online consulting company. We were helping our clients generate revenue through the online channel. And so, There's a few times where we had to reinvent and build new capabilities and shift the company. So I've been through those shifts, I've been through those ups and downs, and I kind of know, I don't know, a lot of what we need to do to— and we're continuing to transform, even though we started with more GenAI stuff, now we're doing more agentic. Now there's this thing called FDEs, or forward-deployed engineers. So there's kind of like some new delivery models that are emerging that we're doing with again, AWS and with other clients. And so we're constantly evolving our services and capabilities and how we deliver it.

 

Len Pagon [00:49:17]:
And even the AI, we were always an engineering-strong organization. What I think is happening is we are now, our whole company are AI builders, and there's a lot of overlap. A UX person is building stuff. A project manager isn't just a product manager, project manager. They're technical. They understand they're building stuff. And so everybody's building it, still leveraging their crafts, but the teams are getting smaller and more productive, they're able to produce more. And so yeah, we're constantly trying to figure out how to optimize, so that's similar.

 

Len Pagon [00:49:57]:
The things that are different, maybe I'll come back to the similar stuff, but the things that are different, there's a number of them that are different. Okay. is I've never been part of a technology that's like nipping at your heels. Like, if you don't figure out where this is going, which we, you know, we just have to be— it's kind of like outrun the bear. I don't have to outrun the bear, I just have to outrun my friend, um, and, you know, be faster than him. And so we— I feel like we're in this race to just be, you know, at least a couple steps ahead of our competitors, a couple steps ahead of our, of our clients, because if our clients know how to do what we do and they're just as good as better. There's no value proposition for us. We have to be better and at least a year or 2 ahead of them in terms of the things that we're doing to be able to add value to them and have that experience and capability and have been there and done it before.

 

Len Pagon [00:50:47]:
The thing that's different is this AI stuff can put us out of— things are getting faster and faster. And so I have this existential thing, like eventually does AI sort of take over what we do. So, it's nipping at our heels and advancing very rapidly than we're able to even learn and adapt. The second thing is it's just never-ending. Like with those other waves, you'd get through the wave in a year or 2 and then things, the innovations were kind of manageable. They were incremental. There might have been like, okay, you have Web 1.0 and then you have 2.0 with social media. They were manageable.

 

Len Pagon [00:51:24]:
And there was new companies that emerged to take advantage of it. Again, this internet stuff is— or AI opportunity, there is just massive flux and change that's nonstop. Now all these agentic tools are coming and now all the work really isn't sort of the building stuff. It's actually looking at what's called the evals and getting the observability and the optimization of those agents because evals our trace of what those agents are doing on each one of their loops. So you can see the work that they're doing and how they're performing. And then you can, you can opt, you continue to optimize those agents and their performance. So again, that's something else with— we now we have data scientists and other capabilities that we didn't, we didn't have, you know, there are some data scientists, now we got a whole bunch of data scientists that work for us.

 

Jeffrey Stern [00:52:12]:
So if, and again, just curious how you're thinking about it, being at the frontier of what's happening right now. If intelligence and even consulting practice sort of becomes abundant, how do you think about what is scarce and where enduring competitive advantage actually accrues? Is it simply just running faster than the person next to you?

 

Len Pagon [00:52:40]:
I think ultimately, so now We're growing very, very rapidly. At the end of last year, our run rate, we had like 90 people. We have 220 people now, 225 or 230, and we have 40 open positions or something like that. And so we have lots of demand for what we're doing. I think that more and more of the value will accrue for the domain expertise within the industries. Today, that's a nice-to-have, it's not a must-have. I think in a year from now, that's going to be a must-have. And I think that the more and more domain expertise you have in those industry verticals, one of ours is energy, one of ours is education, one of ours is financial services, we have another one in retail and consumer goods, we have a handful of verticals.

 

Len Pagon [00:53:31]:
And so right now, that's again, people want to know that you have the industry expertise, but what trumps that is the agentic expertise that we have. And so they're kind of like, all right, we'll bring the domain expertise and you guys just bring your know-how. And so we have, again, the industry verticals. And I think as we move over time, as we were talking earlier with the skills, more and more of the work that we do has the agents and the skills built into it. The services part, the hours, becomes less content. In the past, software and services didn't mix at all. You might have a little bit of IP that you brought to the table, but it wasn't— a software company was a completely different model than a services business. Today, we see ourselves almost as a technology or software company that has a lot of services on top of it.

 

Len Pagon [00:54:26]:
Over time, the tech content is going to grow, the digital work will grow, and it's more about how do you wield those digital workers and orchestrate them is where I think things move over time. And I think more of our work will be fixed cost. I think more of our work will be outcome-based. The model will shift where we're not— the work won't be so much by the hour. It'll be more about outcomes that we— again, and that requires the domain expertise and that you can rinse and repeat the same use cases over and over for different clients that have similar problems.

 

Jeffrey Stern [00:55:01]:
What part of yourself as an operator and CEO had you forgotten about during those almost 2 decades away from being in the CEO seat? And what has running a company again taught you that the years investing didn't? And what can you only learn as an investor?

 

Len Pagon [00:55:28]:
Well, I don't— it's a compound question again. Yep. I guess I'll again ask a question that you sort of— that I think— I don't know that there's anything that I didn't sort of— I forgot. There was nothing I was like, oh, I'm pretty rusty at that. I thought actually that something that I expected to be rusty is like, as a 60-year-old, do I have the energy and drive That was a huge question for me. I had a very comfortable life. I could play lots of rounds of golf and go travel to Europe while I'm working and managing my affairs. And now, work is my priority.

 

Len Pagon [00:56:11]:
My wife says, what are you doing? I'm like, I'm working. I'm working 7 days a week. That question was answered the first week. Even then, I was like, well, can I sustain it? And now, it's 15 months and I'm Working harder than ever. I don't think that there's anything that I feel like, yeah, I was rusty at or forgot. I think the thing that I have a deeper— that I didn't really get the first time around that I have a much deeper appreciation or sense for is just the— and very rewarding, but maybe even the most rewarding is the people. I was more focused on the market and selling and making rain and spending time with clients. That's where I got a lot of my energy.

 

Len Pagon [00:56:57]:
Most of my energy was external to the company. And what I'm finding, again, maybe most rewarding of this go-around is, in fact, it's given me goosebumps, is I can sort of set, again, I'm not even sure, I have this instinct and we might talk about it, like we're AI first, this MZero example that I talked to you about before. And then, we talk about it as a team and then somebody just goes and the team does something that blows me away. And, I'm getting blown away all the time where they just figure— again, it's an instinct or maybe it's maybe not even my idea, but the example about my CFO or my head of marketing, the work that they did just gives me— just the work they're doing just blows me away. Um, yeah. And just how, how hard they're working and how much they love, like they're just on fire. And yeah, I didn't have that. I didn't appreciate that.

 

Len Pagon [00:57:56]:
I didn't have that the first go around. And yeah, it's just, it's awesome.

 

Jeffrey Stern [00:58:02]:
Yeah, no, it is super cool. The, the, uh, the second part to that compound question I am still curious about though, which is, I feel like it's, it's more common a trajectory for—

 

Len Pagon [00:58:13]:
You wanted vulnerability. Like I'm getting emotional.

 

Jeffrey Stern [00:58:16]:
Yeah, yeah.

 

Len Pagon [00:58:16]:
Thinking about my— and my team.

 

Jeffrey Stern [00:58:19]:
Yeah, well, I mean, people are everything.

 

Len Pagon [00:58:21]:
So the other, the other part that I didn't, I didn't get to.

 

Jeffrey Stern [00:58:25]:
Yes, yes. Well, I, I feel like the, the path that is much more common and well-trodden is the one of like the path that you were on of you, you've built this amazing company in Brûlant and you have an amazing exit to Rosetta and you you, you unlock the ability to invest. But I think it's a lot rarer for folks to go from the investor seat back to the operation seat. And but what, what, what you feel like you learned from the investing chapter of your career?

 

Len Pagon [00:58:55]:
Yeah, so actually, it's, it's, it's not maybe not what you're maybe expecting. So I've been on a lot of boards over the years working with CEOs over the years, you know, in our portfolio, and then other boards, you know, Maybe we have a minority position in the business. And so when you're on a board, over the years, I've become a lot more forceful with a few things, like the things I feel really strongly about that I think are important to improve the business. I pick my battles. I don't give them 20 pieces. I might suggest or have some other suggestions, but I've learned to be a lot more provocative or forceful about the things that I think are really I'm going to make a difference. And so, yeah, I make all kinds of suggestions. And in reality, once in a while, CEO or something happens, it's like, oh, okay, I had an impact.

 

Len Pagon [00:59:47]:
So the impact is pretty obtuse or indirect. It's a lot less rewarding. Sometimes you can— there's one company that had 2 brands that they were operating in. And I said, well, you're too small to maintain those 2 brands. And I really think you should collapse them. That was my big feedback for the board. They acquired this company. And 2 years later, they consolidated it.

 

Len Pagon [01:00:09]:
Now, I don't know if my input had anything to do with it, but I was really forceful. I thought, you can't put enough marketing budget behind both of those to be— and even combined, it wasn't a big marketing budget. So let's make the maximum amount. And there was one brand that was clearly a premier over the other. I'm like, we should just focus on that, which is eventually what had happened. But again, it happened maybe 2 years later. And I don't even know if the CEO would credit me with even, you know, me even saying that. But I remember the moments at the board meeting where I was the only one, I was the lone voice.

 

Len Pagon [01:00:41]:
So now being me being in the seat, what I realized is actually my instincts were a lot better than what I thought they were. And, and most CEOs are not— there's some that are exceptional. There's a few of them that are exceptional, but most of the businesses— I know what good looks like, and most of the businesses that I was on the boards was mediocre. They were mediocre leaders of the business. They weren't— I'd say they'd have some CFO come to the board meeting, present, and after 1 or 2 times, I'm like, that guy's mediocre. You got to replace him. You got to upgrade that. And then years later, that CFO is still working for them and the CEO's complaining about a CFO.

 

Len Pagon [01:01:24]:
I'm like, we talked about it. You just get fatigued of saying the same thing over and over. And so the biggest aha for me is just now, when I'm on another board, and I'm still on a few, I'm just way more forceful. I don't have the patience. Either get it or I'm going to get off the board. this is not worth— it's not worth my time just to be saying the same thing over and over.

 

Jeffrey Stern [01:01:47]:
What, what are the few things that you've distilled down that you think make the biggest difference and matter the most?

 

Len Pagon [01:01:56]:
You mean in general for a business or a leader or CEO?

 

Jeffrey Stern [01:01:59]:
Yeah, like when you're on the board, in your mind, you know, you mentioned it's come down to just a few things that you're dogmatic about. What are—

 

Len Pagon [01:02:08]:
You know, I mean, I would say the categories, it's pretty— yeah, there's a lot. There's a few recurring themes. I would say one of them is strategic focus. And strategic focus, I think, really comes down to 2 pieces. One is having a very clear commitment to what you are that differentiates you in the marketplace. And so you have to— and you can evolve it. You craft that thing, right? It's not like I got a lightning bolt and I know exactly what it is. You do it, you test it, you refine it, and you kind of tune it into that.

 

Len Pagon [01:02:44]:
But you can have a dimension to say, this is what's gonna differentiate us, and then you'll figure it out over time. But it's still having that commitment and focus on it. And then the second part of it is what you say no to. And I see a lot of CEOs, they're always chasing stuff, or they're just too committed and they're not adapting. But that strategy piece is— I'll just say like ripe with, again, a lot of not evolving. Like I was with, you know, there was a CEO that preceded the other CEO that I got rid of who was actually a good CEO, but she ultimately couldn't come. I was telling her to, you know, when ChatGPT came out in 2022, like '23, I was like, what are we doing with AI? I don't know what we're doing, but it feels like it's a big, this is a big moment right now. We gotta figure this out.

 

Len Pagon [01:03:29]:
And she didn't really figure it out. My other CEO didn't figure it out. And here now, in a very short amount of time, 15 months, we've gone from being AI capable to a leading provider of doing amazing AI solutions. So that strategy, I think, is a big recurring theme, again, some different pieces of it. And then I think the second area is just around your leadership team and the executive team and having— you're constantly upgrading what somebody might be good to get you from $5 to $10 million or $5 to $20 million. Then you need somebody that has more experience and can get you from $20 to $100 million, may not be the same person. And so I just see a lot of CEOs that settle for mediocre talent on their executive team, or even sometimes incompetent talent. And I'm just like, what are you doing? You got to get— and I think that there's not— I would just add another thing which I wasn't.

 

Len Pagon [01:04:27]:
I push. I'll just say being a driver and having a high sense of urgency, and that comes with, I want timelines, they're making decisions, you're not afraid to make decisions and not afraid to fail. So I think that the velocity of the decision-making and again, driving the business is another piece of it that I think a lot of CEOs are, especially today, I think that's a You're asking about changes. I think many companies need to be acting with a very high sense of urgency now. There's going to be a lot of companies that are like a boiling frog, and they're going to wake up one day and they've been disrupted by some newcomer, agentic-first company that just replaced their business, or maybe disrupted their whole industry.

 

Jeffrey Stern [01:05:12]:
Yeah. Has AI or anything else in this chapter forced you to unlearn anything that you used to feel to be really true?

 

Len Pagon [01:05:23]:
I got one. I have one other thing from an earlier question that just occurred to me about that. Yeah, yeah. Uh, all right, so unlearn, unlearn, we'll come back to that. I don't want to lose this thought. You were asking about, is there anything that you forgot, you know, since the last time? Yes, I took it as a CEO, um, but I started my career as an engineer, so I, you know, went to Case I started out in biomedical engineering and then finished with electrical engineering. And so, I was an engineer. I used to do coding when I first started my business.

 

Len Pagon [01:05:49]:
I used to code and be very tech. I built stuff. And so, for the first time in 40 years, I'm building stuff again and I'm deploying stuff to GitHub and I'm building stuff and there's more things that I want to build. And I'm forcing myself to be more of a technical leader than I had been in the past. I want to understand this agentic stuff. I want to understand what evals work and how you optimize latency with token costs. And I want to see those things kind of in action. I want to have a more intrinsic understanding of those things.

 

Len Pagon [01:06:31]:
So that's the one thing that I'd forgotten. There's a bunch of latency stuff back there. I got to keep in mind. It's really one of the— I used to do assembler code. I used to be able to write assembly once upon a time on the microprocessors. So, I can definitely sort of figure it out. And, I think it's important to understand. I don't need to be as good as my engineers, but I think there's value in understanding the— yeah, just what this— it'll help me understand what this stuff can do and where it's going a bit.

 

Len Pagon [01:06:58]:
What was the question that you actually asked that I— About unlearning. Something on learning?

 

Jeffrey Stern [01:07:03]:
About unlearning.

 

Len Pagon [01:07:04]:
So, say that question again.

 

Jeffrey Stern [01:07:06]:
Yeah, well, just before I do, I mean, I, I do think that's one of the most beautiful things about AI is just how it's created this path for a lot of us to come back to engineering, uh, in a way that, that has been sort of prohibitive.

 

Len Pagon [01:07:22]:
Yeah, creating your build, you're building, you're, you're building stuff. It's, it's awesome. It is really cool. My, uh, you know, Tony, my CFO, is, is, yeah, building amazing stuff. You know, Christina, the The marketing person, leader, executive is just building stuff and they're so proud of what they're building. And everybody's like, you know, she built this bot for this thing we did called Tommy Two Phones with this Italian personality and answered all these questions with a bunch of Italian sass. Like it was, and we just had like so much fun with it. Yeah, it's just, you can see the pride where people are building this cool stuff and they're working hard to do it.

 

Len Pagon [01:07:54]:
It's, you know, in their spare time.

 

Jeffrey Stern [01:07:58]:
So to rephrase the question, right, you have all of this galvanized knowledge, earned wisdom from your experience. Has AI, or again, really anything in this chapter though, forced you to unlearn anything that you had previously held as truth?

 

Len Pagon [01:08:21]:
You know, I don't know that I would say unlearn, But I definitely think it's changing mental models. There's a lot of mental models that I had that are— I have to create some new mental models. So, mental models are the way that sort of shape your assumptions and shape the way you think. And sometimes they're invisible. You're not even aware they're there. And yeah, so I guess that's a way of unlearning stuff. Like, you have a certain way that you see the world, and now you got to sort of see things differently. Yeah.

 

Jeffrey Stern [01:08:52]:
I think you need a different heuristic.

 

Len Pagon [01:08:54]:
Yeah, different heuristic. And I think the rate at which we have to learn and how you learn, like learning is by doing. Like I used to think reading is important, but 80% of your learning is actually like getting right in there. That's how all the neuroplasticity and all the neurons firing and connecting is mostly. It's like if you wanna learn a language, immerse yourself and just force yourself to speak that language for a month, you can be way farther along than looking at Duolingo 2 years. You'll learn vocabulary, but your brain won't really rewire unless you force that work, that cognitive work to be done. And so I feel like a mental model was I thought learning was like, okay, you go to training, you have a counselor, and they give you some mentoring, and you do some reading, and you look at some tutorials, and then you kind of— the mental model now is learning is part of the work. Learning is almost all the time, everything you do, is learning and you're experimenting and building because you can do it quickly.

 

Len Pagon [01:09:55]:
You can build a skill and say, oh, how did that perform? And then you can try something else or try a different model or do something else. You can just constantly try stuff and in a very short amount of time, you're getting feedback. Or ask the models. I'm surprised at how still I'll be doing it the traditional way and I'm like, Well, shit, I can just— if I just ask the model the right way and then the model teaches me or does something that I wasn't even thinking to do. And so for many things, working with AI is a default. There's still a lot of things where I'm doing it some other way and then like, duh, let me try it, try it, try it. And then you'll figure out a way that the model can actually do it. And so Yeah, there's— and just like the mental model of embedding the skills, like that's a new thing for me.

 

Len Pagon [01:10:51]:
Like I didn't— I was still kind of thinking of it the old way where you're going to do it and then you build it and you sort of share it and you got to train everybody on it. And now I can sort of embed. And now I think about what's the intelligence that I have that I can actually build in that everybody can take advantage of, not just, you know, and start to share that, that's where you start to unlock huge power where you're sharing somebody's domain expertise and special specific expertise and skills. And I think I can bet some percentage of that. Now, everybody can take advantage of that without— I create tremendous leverage. I think another new mental model is I think of it as huge leverage for me more than just having digital workers and agents doing stuff on my behalf, but how can I create stuff that's in my brain and actually incorporate that into something that other people can leverage. There's just, I can go on. There's like a lot of shifts or different mental models that you weren't thinking about it.

 

Len Pagon [01:11:50]:
And I don't even think the powerful stuff has even happened yet. When we start doing things with agents and we start just offloading and having these digital workers, like I think all of a sudden a whole bunch of other things kind of unlock and change and transform.

 

Jeffrey Stern [01:12:04]:
Yeah, I mean, we could riff on AI for a long time. It's sort of endlessly fascinating. We'll bookend it though with maybe a final AI question and then we'll work to close it out. But I mean, what to you feels most unknown and uncertain today about AI that— maybe another way, What question about AI do you still feel like you really don't have a good answer to?

 

Len Pagon [01:12:36]:
Yeah, I mean, I just don't know. I think about the bigger questions. You know, a big question is, I don't know what percentage of work gets transformed. I can see how a lot of it is being, on one hand, I can see how a lot of it gets moved and moved into AI. I also see lots of friction and constraints, going back to the data quality, the pace at which people move. We're not moving anywhere as fast as I think we can move, but we're moving way faster than at least anybody that I know, like the progress that we're making. And the stuff that we're thinking about, or working on. I think a lot about what is the near-term undoing.

 

Len Pagon [01:13:33]:
One thing that I forgot to mention about the rhyming part that hasn't rhymed yet is all the other technology buildouts always have this big hype cycle, and then there's a crash. So you have it with the dot-com, is the most pronounced, the dot-com bubble, and then you had a crash. And so If you look at the data center buildout right now, all this infrastructure, for about over an 8-year period of the peak of the internet buildout was about $2 trillion that was spent. And if you do that same estimate over for AI, it's like 5 or 6 or 7 times that. And people think, they're talking about SpaceX data centers in the sky, and you have all this data compute capacity that it's going up. And so I think a lot about I think it'll rhyme. I think there's going to be some major correction. A difference is the dot-com bubble, there was a whole bunch of businesses that weren't making any money, and you just had all this capital flowing in, and everybody was spending between each other.

 

Len Pagon [01:14:30]:
That kind of inflated or conflated all this equity value that just deflated when people didn't perpetuate that. Now, there's companies making tons of money, like big lots of revenue growing really fast, faster than what we've ever seen in the history of, I'll say, at least businesses and capitalism. And so, yeah, what is the undoing of that? And so, is it the models that get so much better? Now we have open-source models and the intelligence at certain token costs actually undo the need to do all this, to do this build-out. Does the dislocation of all the jobs, create a huge depression. So now I have 15% unemployment, and now that, you know, AI actually creates its own recession, and the capital dries up because there's not a bunch of, you know, consumers that are, you know, so I, there's a, there's a number of things that can undo it. But I think that'll rhyme. I think whether that's a year from now, or 3 years from now, I don't know, or 5 years, who knows. But I think you're going to see some major correction.

 

Len Pagon [01:15:33]:
I think you'll see Nvidia stock be 50% of what it's trading at today for some reason. And I think you're going to see a major correction at some point, and you'll see this compression of all this capital spending. It might be capital that just dries up. It just can't perpetuate. You see now the big hyperscalers that are spending hundreds of billions of dollars, and now they're doing all these bond raises. Maybe that dries up and they just have to hunker down and get the value out of their spend that they already had, and that sort of dries up. Yeah. So there's 2 things in there.

 

Len Pagon [01:16:06]:
I think about, again, what creates a recession and the job dislocation, including us getting dislocated, or our friends and family. Or I think about, again, what sort of is this bubble burst? And then beyond that, I do worry about people using this for nefarious means. with these open models, they don't have the guardrails that Anthropic has or OpenAI has. They're almost as capable as those models, and you can do almost anything in them. And so whether you're building biologic weapons or doing stuff with fakes and doing cybercrimes, and I don't know, there's a bunch of nefarious stuff that could happen with these open models being everywhere. Yeah, so I worry, I worry about, you know, those kinds of things. But those are the, those are the bigger kinds of, kinds of things. Right now, I'm just sort of, how do we build the business? We're having a lot of fun.

 

Len Pagon [01:17:06]:
I try not to think about those 2 things. Those things.

 

Jeffrey Stern [01:17:11]:
Those are, those are existential. I mean, it is hard to—

 

Len Pagon [01:17:16]:
I don't think, you know, one thing— I'll go finish your thought, Jeffrey. I'm sorry, I interrupted you.

 

Jeffrey Stern [01:17:20]:
No, I just, I'm constantly reminded because I you know, I got to live it through reading more than experiencing it. But like, I mean, we're living in the largest infrastructure project, maybe in recorded history.

 

Len Pagon [01:17:33]:
Like, it is not even close.

 

Jeffrey Stern [01:17:35]:
It's, it's a little, it's crazy, right? Like, it's, it's wild.

 

Len Pagon [01:17:40]:
Yeah. Um, there was something I was gonna say. I don't know. It doesn't matter.

 

Jeffrey Stern [01:17:48]:
Is there, is there something you wish we had talked about here that, that we haven't gotten to touch on yet?

 

Len Pagon [01:17:54]:
I don't know. I mean, the only thing, if, if I was like to say there— yeah, I could talk about, you know, a bunch of stuff, but I think, uh, I just think we're, we're living in a great time. I, I, you know, I, um, and maybe this is the, the best time. Like, the, the, this is, you know, a lot of people are— my friends are worried, maybe social media, they're reading It's too much stuff about politics or whatever. But, I think all in all, this is an amazing time to be alive. Yeah, I'm just supercharged up to get out of bed every morning. I think the biggest thing that I would say for people is just to be a learning machine, an ultra-learning machine. Yeah, because I think that and using, just embracing learn by doing and using these tools for as much as you can.

 

Len Pagon [01:18:44]:
Just because the people that are not going to— going back to the recession, it's the people that know how to use AI really well are going to be just fine. They're going to figure it out. They're going to have new skills. They're going to have new jobs and new roles. They'll be doing new things, but they're going to be just fine because they know how to use the tools to amplify themselves. The people that are reticent to learn, are the ones that are going to be significantly at risk. Whether you're in manufacturing jobs and robotics are going to be a whole other wave that they're not going to disrupt you anytime soon, 10 years from now, yeah, robots are going to be all over the place. And so, it's learning how to use the tools that is going to be, I think, the biggest opportunity to amplify yourselves and kind of reskill and figure out how to live in this modern world.

 

Len Pagon [01:19:39]:
And so, yeah, reading, listening to podcasts, just using these tools is the best way to do that. And I think the best— what I tell college kids that they want career advice, they are always asking me, what are the careers that are going to survive this AI stuff? And I'm like, I ask them, what are you doing? Tell me what you're doing with Claude Code. And they're like, well, I do this. And I'm like, Man, you should be building all kinds of stuff. You know, be the— start an AI club at your college. Like, go learn how to, like, lean into that. That's what's gonna— that's what's gonna create your future in whatever you want to do.

 

Jeffrey Stern [01:20:15]:
Yep, certainly resonates. Well, I'll ask you then our traditional closing question, which is for a hidden gem in Cleveland.

 

Len Pagon [01:20:26]:
Oh, this is the hidden gem. I forgot about— you warned me about this question.

 

Jeffrey Stern [01:20:31]:
The hardest question that I've asked so far.

 

Len Pagon [01:20:35]:
Is it a hidden— does it have to be like food or just a hidden gem? Anything?

 

Jeffrey Stern [01:20:39]:
Anything. Yeah, something you, you wish more people understood about Cleveland that, that maybe they don't.

 

Len Pagon [01:20:45]:
Well, I would, you know, I— if it's somebody from outside of Cleveland, I mean, I, I think we have lots of hidden gems. I mean, our Playhouse Square, we've been a big supporter over the years. We have one of the top Broadway series in the world. I think it's like number 2 or number 3 after New York City. We have amazing programming, amazing facilities here locally. Same thing with Severance Hall, one of the top 5 orchestras in the world that are here. And yeah, just, I mean, the Rock and Roll Hall of Fame, everybody kind of knows that one, so that's not too hidden. Our metro park systems are amazing.

 

Len Pagon [01:21:25]:
The metro parks are amazing hiking trails, beautiful biking. We have abundant, lots of them. And so, yeah, from somebody from outside that wants to move back or move here, there's just amazing assets. It's been a phenomenal place to build a business. I built my other business here and now we're building another amazing business. We have so many smart hardware and it's the work ethic. And, we're just a bunch of lunch pail carrying grinders that just show up to work and work hard and get stuff done. We just have a phenomenal work ethic.

 

Len Pagon [01:22:03]:
And so, yeah, I just think it's a great place to be headquartered and have an amazing business. In terms of if somebody's a local, I don't know, they're not my favorite places. I love Crew, which isn't Yep. He's in a hideaway. Yeah, I don't know. Okay. Yeah, Crewe's one of my favorite places. Like, I love it.

 

Jeffrey Stern [01:22:27]:
Awesome. Well, and this was a lot of fun, first off. And I do very much appreciate you taking the time to just come in and share how you're thinking about the world these days and what you're building.

 

Len Pagon [01:22:41]:
What was that? What was your question? Your hideaway? What was that? What was your phrase you used? Sorry, you, you, my last— what was your last question again? I haven't— I have— Oh,

 

Jeffrey Stern [01:22:49]:
about hidden gems.

 

Len Pagon [01:22:50]:
Hidden gems. I think, I think Chagrin Falls, downtown Chagrin Falls is a hidden gem. Like, I think every time we bring in guests, they're just like blown away. Like, oh my gosh. And then the restaurants that are there with Lola's Bistro and Lola, or JoJo's and 17 River, like, they're just— I just think downtown Chagrin Falls, especially in the summer, doesn't get too much better than that. Jenny's Ice Cream. It is a remarkable place. Like all of it.

 

Jeffrey Stern [01:23:18]:
Yep. That's a perfect one. That's all for this week. Thank you for listening.

 

Speaker C [01:23:25]:
We'd love to hear your thoughts on today's show. So if you have any feedback, please send over an email to jeffrey@layoftheland.fm or find us on Twitter @PodLayofTheLand or @SternFA. J-E-F-F-R-E-Y. If you or someone you know would make a good guest for our show, please reach out as well and let us know. And if you enjoy the podcast, please subscribe and leave a review on iTunes or on your preferred podcast player. Your support goes a long way to help us spread the word and continue to bring the Cleveland founders and builders we love having on the show. We'll be back here next week at the same time to map more of the land. The Lay of the Land podcast was developed in collaboration with The Up Company.

 

Speaker C [01:24:04]:
LLC. At the time of this recording, unless otherwise indicated, we do not own equity or other financial interests in the company which appear on the show. All opinions expressed by podcast participants are solely their own and do not reflect the opinions of any entity which employs us. This podcast is for informational purposes only and should not be relied upon as a basis for investment decisions. Thank you for listening, and we'll talk to you next week.