00:39:01 · Mikk
[00:39:01] S0: It's all great. Business is risky. Entrepreneurship is risky. Using AI seems like a no brainer. What are some of the downsides or risks when when when doing and implementing all this?
00:39:16 · David Flickinger
[00:39:16] S1: The the first one that comes to mind is cost. So so cost is a huge one. And for a small subset of business, it's data security. I think data security is is, one that's a risk for everyone. But, of course, on the spectrum of lower middle market companies, maybe someone who's doing $5,000,000 a year is less concerned with their customer data being sent over an API to OpenAI than a business that does $500,000,000 a year. That business, no matter what industry it is, might actually be concerned with customer queries that are unsanitized going across to Gemini, Claude, Anthropic, all in pursuit of... Well, hopefully, those companies now soon to be public have good security policies themselves, but maybe you're making those businesses smarter or you're at very least sending customer information that your customer thought was proprietary out into the world. So there is a data security layer, and we deal a lot with health care businesses. My background is in health care. So we've thought a lot about that. We essentially have this anonymizer or sanitizer that we can plug into this architecture that will remove either patient information or sensitive customer information when you send an API request. So data security is a big one for the right sized businesses. I'm not saying everybody needs a sanitizer on top of their agentic architecture, but everybody should be concerned with cost. And I think in the news, there's been several articles now. Microsoft canceling a bunch of Claude code subscriptions was probably the most notable one recently where I think in the absence of this context layer I described, these agents are using the most powerful models to just bludgeon through, and it's very inefficient, and it's very, very expensive. And so I think one needs to think about how you deploy AI with the right model for the right task. And I'll give you an example. So let's take this roofing estimator as an example. You have this AI orchestrator. Maybe you wanna use Mythos or Opus 4.8 or GPT 5.5, whatever the latest model is that you wanna use. You're really smart model as your brain. But if you're reading PDF specs that just need summarization, well, maybe I don't need the latest and greatest for that. Maybe an open source model could do that. Most of these open source models are as good as the frontier models were six months ago, so they're really good.
00:42:04 · David Flickinger
Maybe I can use that for free. Maybe I have now spreadsheet extraction or I'm double checking spreadsheets. I could probably use a Gemini model from two iterations ago that's really, really good at math, is excellent at Python, knows spreadsheets, and it will do a great job but for fractions of a penny. Whereas if I did that with the smartest model, I would be racking up tons of costs. I'll give you an example in one of the businesses that we engage or have an engagement with doing document generation based on a workflow. It was a few cents to do document generation using the latest version of Claude, and that jumped up to about $14 per iteration over the last few months. So these companies, OpenAI, Anthropic, x AI, SpaceX, They're they're soon to be public companies, and they have shareholders now to answer to, and they have to start making the economics work. The only way to do that is to continue to release top models for which they can charge high per token or per usage fees. And so if you as a business owner wanna say, well, I'm just gonna sign up with Claude or just gonna sign up with with Anthropic or OpenAI, and I'll just use that for everything. That could be a very, very expensive mistake. We're already seeing companies come to us and say, yeah. I got my bill from Anthropic, and this is insane. I don't know how people are using AI in their business. Well, what they are doing is they're putting everything through the smartest model. What you wanna do is you wanna have smart tasking like I described. And to the extent possible, you wanna use what are called local models, which don't go outside the walls of your building or maybe they live on a cloud server, but they're not going across. And so if you have sensitive customer information or you're concerned about cost, routing tasks to a local model can be a great option because there's no usage fees for those. The usage fees are are baked in to whatever you spend to to run that model, whether it's a local server on premise or whether you use that in a cloud server, you could use it as much as you want without any additional cost. So I think cost will continue to be probably the top point of discussion and concern among both large and small businesses as they think about AI deployments.
00:44:46 · Mikk
[00:44:46] S0: David, remember I said at the very beginning of this episode that I want this business owner, investor, whoever is listening to to really forward this episode to to their portfolio company or their cofounder to just keep these AI ideas going for them. That being said, I want to finish with you getting even maybe more practical. Let's say there is someone listening who runs one of those traditional businesses, 1 to 10,000,000 of EBITDA, and they would like to do something within the next maybe maybe thirty days. So they have a bit of like a before and after. What would be something you you suggest them to do?
00:45:30 · David Flickinger
[00:45:30] S1: So the first thing I suggest every business do is connect an agent to your systems so that when you start to interact with AI, you can have an answer that's relevant to your business. That first step is easier than one might think. So that's what we do as a first step engagement for a lot of businesses. And then you can start to think about which work streams, whether that's roofing estimating or medical device compliance or some sort of collections reporting that you wanna automate as a particular work stream, maybe as an agent loop. You'll start to hear more about these agent loops, which you essentially set goals and have an agent run within your architecture to continuously iterate. That's step two, three, four, five. The first thing is connecting your systems into an agent that you can interact with so you can start to ask questions of these powerful AI agents, these powerful AI models that have context about your business. So that'd be the first thing I would recommend every business owner do, and there are a couple open source tools that folks can use. Folks have probably heard about this OpenClaw idea that became really popular and then kind of fizzered... Fizzled out. And I still use my OpenClaw here and there. But what you really find is at an enterprise or business level, those don't really cut it. They're probably not secure enough either, but they're really not built for real enterprise or even small business. They're built for individual use cases. But you could certainly, as an individual, if you have a really, really small business, try to do that. The problem with those is they could be technologically overwhelming to set up. You almost have to be a developer in order to get those working properly. And most folks I've talked to have tried to implement OpenCLaw successfully who are not developers who have just spent 10 times more effort and energy trying to get it to work than they have actually using it. But my point would be connect your agents to your systems as the first thing you do before you try to do anything else with AI in your business.
00:47:58 · Mikk
[00:47:58] S0: What I learned today is if if done well, if AI integrated in a correct way, you can make a simple business a lot more valuable by just having all the information in one place, making better decisions, sending out more and better proposals. So that being said, I I know you're busy, but maybe maybe someone would like to reach out. What would be the best way for them to to contact you?