Every so often a technology arrives that does not merely improve the tools we already have but changes the nature of what can be built at all. The personal computer was one. The internet was another. The mobile phone a third. In each case, the companies that defined the decade after the shift looked almost nothing like the ones that came before — and the investors who understood the change early were the ones positioned to own them. I believe artificial intelligence is the next of these turns, and it is the reason I left more than two decades in traditional asset management to dedicate myself entirely to it.
What makes this moment different is that intelligence itself — the reasoning, drafting, analyzing, and deciding that used to sit exclusively in human hands — has become a resource software can draw on directly. That is not an incremental feature. It changes the cost structure of entire categories of work, and by extension the economics of the businesses built on top of them. For a management team, it means a lean operation can now attempt what once required an army. For an investor, it means the map of where value will accrue is being redrawn in real time.
A generational platform shift
Platform shifts are rare and they are humbling, because they invalidate a great deal of accumulated wisdom. The playbooks that worked in the last era — the assumptions about how long products take to build, how defensibility is earned, how quickly a market can be reached — all have to be re-examined. AI compresses timelines that used to be measured in years. A capability that would have been a multi-quarter engineering effort a decade ago can now be prototyped in an afternoon, which means the questions that separate a durable company from a passing demo have moved elsewhere.
That compression is exactly why the shift rewards conviction over consensus. By the time an opportunity is obvious to everyone, the early advantage is gone. The firms and management teams who win in a platform transition are usually the ones who acted while the picture was still forming — when moving early required judgment rather than certainty. The name Fifth Turn reflects that view: AI is another turn of the wheel in how technology reshapes the economy, and turns of that magnitude reward those who commit before the crowd arrives.
How AI changes private equity
The mechanics of investing change in subtle but important ways when the underlying technology is moving this fast. Traditional diligence leans heavily on historical performance, and that history still matters — but in a market where a product can be reconceived every few months, backward-looking metrics tell you less than they used to. What matters more is a management team's rate of learning: how quickly they absorb what the technology can now do, how honestly they confront its limits, and how fast they translate that into a better product.
Capital efficiency also looks different. When a handful of engineers can ship what previously demanded a large organization, the resources an established company needs to reach the next set of proof points fall — and the discipline to stay lean becomes a genuine competitive edge rather than a constraint.
"The best AI operators treat capital as a tool to compound learning, not as the thing that defines their ambition."
What durable AI companies share
Not every company that uses AI will be a durable business, and separating the two is the central work of investing in this moment. Over time I have come to look for a few traits that tend to travel together in the companies most likely to last.
- •AI at the core — Intelligence is the product, not a bolt-on feature; the business is built AI-native from the first line of code.
- •A real workflow — The technology is aimed at work people actually do and would pay to have done better, not at a demo that impresses in isolation.
- •Compounding advantage — Every user, every interaction, every proprietary loop makes the product measurably harder to replicate.
The thread connecting these is a refusal to mistake capability for value. It has never been easier to build something that looks remarkable in a controlled setting. The harder and more valuable thing is to put modern machine intelligence to work inside a real business workflow — where it has to be reliable, accountable, and worth paying for month after month.
Why an operator-investor perspective matters
I do not evaluate AI companies only from the outside. As CEO of Flowlinx, an AI company building AI-native software that puts machine intelligence to work inside real business workflows, I spend my time on the same problems the management teams I meet are wrestling with — where the technology is genuinely reliable and where it still needs a human in the loop, how to price something whose costs move underneath you, how to earn a customer's trust when the product is probabilistic rather than deterministic. That dual vantage point matters because AI is moving too quickly to understand secondhand. Having sat in the operator's chair, I can tell the difference between a hard problem being solved and a hard problem being narrated.
How Fifth Turn approaches the moment
At Fifth Turn Capital, we invest with conviction and we stay close. We keep our portfolio deliberately focused so that management teams get real attention rather than a name on a cap table, and we pair capital with the operating perspective that comes from building AI software ourselves. Two decades in investment and portfolio management taught me discipline and how to think about risk across cycles; the CFA framework I trained in taught me to separate a durable business from an exciting story. Our thesis is simple to state and demanding to execute: partner with the management teams of established AI-native companies and help them grow, supporting them with more than money. Read more about our approach.
If you want to know more about the people behind the firm, including my background as both an investor and an operator, that context is on the team page.
A forward-looking close
It is tempting, in a moment this loud, to assume the story is already written — that the winners are decided and the opportunity has passed. History suggests the opposite. In every prior platform shift, the categories that came to define the era kept reordering long after the shift began in earnest. We are, by any honest reading, still early. The turn is underway, the tools are in the hands of a new generation of operators, and many of the most important agentic AI companies of the coming decade are already established and serving real customers — often led by teams no one has heard of yet. If you are one of those teams, or an owner thinking about your next chapter, we would like to hear from you.
