I think the easiest way to explain how I ended up working in AI is that I have always been interested in systems that are not working as well as they could.
That started in healthcare.
I graduated from Parker University in Dallas in 2013 as a Doctor of Chiropractic. I also had undergraduate degrees in anatomy and health and wellness, and at that point I assumed the direction of my career was probably fairly obvious. I was going to be a chiropractor.
And I was.
I practiced for years. I treated a lot of patients. I learned a lot about people, pain, health, communication, and the weird reality of trying to run a healthcare business at the same time that you are trying to take care of people.
But pretty early on, my interests started moving beyond just treating patients.
I took over a struggling chiropractic clinic early in my career. The business was losing money. Within about six months it was profitable, and eventually we were seeing more than 300 patient visits a week.
That experience probably changed the direction of my career more than I understood at the time.
I liked treating patients, but I also really liked figuring out why the clinic was not working.
Why were people not scheduling?
Why was the staff frustrated?
Why were patients falling through cracks?
Why was money being lost?
Why was something that should have been simple becoming complicated?
Sometimes the answer was marketing. Sometimes it was staffing. Sometimes it was scheduling, communication, patient flow, pricing, follow-up, or just a bad process that everyone had gotten used to.
I became interested in the machine around the work.
That kept happening.
I built businesses outside healthcare. I founded a pest-control company and learned direct sales, phone sales, hiring, training, and how differently a business behaves when you are trying to operate across multiple states.
I consulted with healthcare companies, startups, supplement brands, and other businesses.
Later, as COO of Infinity Functional Performance, I worked across telemedicine, referral networks, inventory, operating procedures, and multi-location expansion.
Again and again, I found myself looking at the same kind of problems.
A business owner thinks he needs another employee, but what he actually needs is a process that does not require him to answer every question.
A company thinks it has a sales problem, but the real issue is that nobody is consistently following up with the leads they already have.
Someone buys new software to make things easier and then realizes the software just created one more place they have to check every day.
A lot of business problems are not dramatic.
They are just little pieces of friction that pile up.
That is probably the connection between my healthcare career and what I am doing now.
It is less of a pivot than it looks like from the outside.
When generative artificial intelligence started becoming genuinely useful, I did not look at it and think, I want to become an AI guy.
I thought about all of the annoying things I had dealt with for years.
Information living in five different places.
Emails nobody responds to.
Tasks that exist in somebody's head.
The owner being the only person who knows why a decision was made.
Employees asking the same question over and over.
Important things being forgotten because they were mentioned in a conversation and never put anywhere.
Artificial intelligence suddenly looked like it might be useful for some of that.
Not because everything needed AI.
It definitely does not.
But AI could read things.
It could organize things.
It could remember things.
It could summarize.
It could help move information from one place to another.
It could help make decisions.
It could make software that used to require a very rigid interface feel more flexible.
And maybe most interestingly to me, it could help with one of the biggest problems I had seen in small businesses over and over again.
Too much of the business lives inside the owner's head.
The owner becomes the operating system.
Everybody goes through them.
They know which customer is upset.
They know which employee needs help.
They remember what was promised.
They know what needs to happen next week.
They know why that weird exception exists in the billing process.
And eventually they become the bottleneck.
That idea has become a big part of how I think about AI now.
I am less interested in replacing people with artificial intelligence than I am in reducing unnecessary dependence on people for work that should not require them.
That is a big part of why I started Trinity River AI.
Trinity River AI is based in Lewisville, Texas, in the Dallas-Fort Worth area.
The company is focused on helping small businesses use artificial intelligence in practical ways.
That sounds broad because it is broad.
I do not think most small businesses need an "AI transformation."
I think they need somebody to sit down with them and figure out where their business is wasting time, losing information, repeating work, or depending too heavily on one person.
Then you figure out the simplest way to fix it.
Sometimes that is AI.
Sometimes it is automation.
Sometimes it is connecting two pieces of software.
Sometimes it is building something custom.
And sometimes the solution is just changing the process.
I like starting there.
What is actually annoying?
What keeps happening?
What does the owner have to do every day that they should not still be doing?
Where do customers get stuck?
Where does information disappear?
What is somebody manually copying from one system into another?
That feels much more useful to me than beginning with a list of AI tools and trying to convince a company that they need them.
I am also building Original Effort, which is the place I have created for all of the other things I make.
Original Effort is harder to define because it is intentionally broad.
It is an invention and venture platform for physical products, software, business ideas, and whatever else comes out of paying attention to things that are unnecessarily frustrating.
I have always had a lot of ideas.
For a long time they mostly lived in notebooks, conversations, screenshots, or somewhere in my head.
Original Effort is my attempt to give those ideas somewhere to go.
Some will become products.
Some may become companies.
Some may get licensed.
Some will probably never go anywhere.
That is okay.
The important part for me is actually making them visible enough to evaluate.
AI has made that process dramatically easier too.
I can go from an observation to a product concept, a visual, a prototype, a website, or a working software experiment much faster than I could a few years ago.
That has probably changed my relationship with creativity as much as anything.
I do not have to decide whether an idea deserves six months of my life before I can see what it might become.
I can build enough of it to find out.
That is also where a lot of my current work overlaps.
Trinity River AI is about improving businesses.
Original Effort is about building things.
My personal site is where I am trying to document more of the thinking behind both.
I am still a chiropractor.
That is part of who I am and how I learned to think.
I spent more than a decade treating people, running clinics, and working inside healthcare.
I do not really see the AI work as replacing that history.
It is built on top of it.
Healthcare taught me to look for the actual problem instead of just the obvious symptom.
Business taught me that most systems are held together by people compensating for bad design.
Entrepreneurship taught me that you can usually build something better if you are willing to question why things are done the way they are.
And AI has given me a much larger set of tools for doing something about it.
I am still figuring out exactly where all of this goes.
That is part of why I want to write more of it down.
I am building Trinity River AI.
I am building Original Effort.
I am working on software, physical products, healthcare ideas, and a number of experiments that do not fit neatly into one category.
Some of them will work.
Some will not.
But there is a pretty consistent thread through most of them.
The question I keep coming back to is pretty simple:
What is not working as well as it could, and what could we build instead?
