There are effectively four kinds of companies in the world today.

The first group knows AI exists — at this point, only someone living in a cave could have missed it — but their reaction is essentially: “It’s not for me.” They do not think they need it, they do not have the time or interest to learn it, and they see it as unnecessary rather than worth investing in.

The second group knows AI matters, but has not really acted on it. Management understands there is value to be captured, but the business is busy, priorities pile up, and implementation keeps getting pushed aside.

The third group has started using AI, but mostly around the edges. They may introduce a chatbot, automate a few administrative tasks, or give employees access to AI tools, while the way the business actually operates remains largely unchanged.

Then there is the fourth group: the vision-driven companies.

If we were building this business from scratch today, knowing what AI can already do and where it is going, how would we design it differently?

That is where the real opportunity starts.

Even today, it is still possible to buy a profitable, owner-operated business with an outdated website, slow customer response times, manual workflows, inconsistent processes, weak reporting, and very little automation.

And somehow, many of those businesses still make very good money.

For years, that has created a fairly straightforward value-creation opportunity. Buy a good business and improve the way it operates: build a better website, professionalize sales, introduce standard operating procedures, automate repetitive tasks, improve customer follow-up, implement better systems, and give management better visibility into what is actually happening.

The product or service does not necessarily need to change. The business simply needs to operate better.

AI makes that opportunity much bigger.

There are already thousands of profitable companies that either do not use AI, use it superficially, or simply have no clear strategy for integrating it into the way they work. The opportunity is not just to add a chatbot or automate a few tasks. It is to rethink the business around what can now be done faster, better, and at much greater scale.

A traditional company may still have employees manually answering inquiries, preparing quotes, updating spreadsheets, scheduling work, reviewing documents, writing reports, following up with customers, moving information between systems, and making decisions with incomplete data.

Increasingly, much of that work can be automated, augmented, or supported by AI.

That changes the economics of the business.

Customer response can become faster. Lead conversion can improve. Employees can produce more. Management can see the business more clearly. Processes can become more consistent. Growth can continue without headcount increasing at the same rate.

Most importantly, AI can allow an established company to operate with the speed, information advantage, and operating leverage of a much more sophisticated organization without having to rebuild the underlying business from zero.

That creates two especially attractive opportunities.

The first is acquiring strong, profitable businesses that have not yet made this transition and using better operations, technology, automation, and AI to materially improve them.

The second is partnering with existing companies and helping them make that transformation themselves.

In many ways, this is simply the next version of a familiar private-equity playbook.

Historically, value was created by buying an under-optimized business and professionalizing it.

Now the opportunity is broader.

The next generation of value creation will come from taking good businesses that were built for the pre-AI world and redesigning them for the AI world.

Our Own Experience

We started by using ChatGPT as a very sophisticated, time-saving version of Google Search.

Then we moved into AI agents and discovered how quickly a small team — or even one person — could build powerful tools and workflows that would previously have taken a huge amount of time, money, and technical resources.

That was exciting. But it also exposed a problem.

We were building increasingly sophisticated systems that consumed large amounts of compute and tokens without necessarily creating much economic value. Worse, some of those tools were starting to distract us from the actual work we were trying to improve: our research and decision-making.

That changed the way we thought about AI.

We realized the real opportunity was not simply to build better tools. It was to build better processes first, and then use AI to automate, scale, and continuously improve those processes.

Does this actually make the business better?

Does it increase revenue? Reduce cost? Improve decision-making? Save time? Increase output? Give us better visibility into what is actually happening?

Once we started thinking that way, AI stopped being a technology experiment and started becoming part of the operating system.

We began redesigning how we research, organize information, measure inputs and outputs, test assumptions, and make evidence-based decisions.

Over time, those systems evolved into what we now call our internal Mission Control — a central operating layer designed to help us understand what is happening, decide what matters, and allocate time and capital more effectively.

Improve the process first, measure the economics, and then use AI to amplify what works.

That is the model we intend to apply to the businesses we build, acquire, invest in, and help transform.