Careers in investment management are built on repetition: decades of allocating capital, measuring risk, and being held to account by results that arrive whether you are ready for them or not. I spent more than twenty years in that world — in investment and portfolio management, most recently at Richmond Capital Management — and earned the CFA charter along the way. It was a career I valued and a craft I still respect. So the most common question I hear now is a fair one: why leave all of that for AI-focused private equity?
Two decades of discipline
Professional portfolio management is an education in humility. Markets do not care about your thesis. Over twenty years you learn what actually protects capital: process over impulse, diversification with intent, position sizing that respects what you do not know, and the willingness to change your mind when the facts change. The CFA framework formalizes much of this — valuation, risk, ethics — but the deeper lessons come from living through cycles, watching confident narratives collapse, and seeing unglamorous businesses compound quietly for years.
Those lessons do not expire when you change asset classes. If anything, they matter more when a technology is moving this fast, where the noise is louder and yesterday's numbers explain less than they used to.
Why I made the leap
The decision to leave traditional asset management was not a rejection of that world. It was a recognition that something rare was happening outside of it. The more time I spent with modern AI systems, the more convinced I became that this was a generational platform shift — a change in what software can do, on the scale of the internet itself — and that the most consequential decade of company-building I would ever witness was starting whether I participated or not. I did not want to watch it from the sidelines, allocating around the edges of an old map while a new one was being drawn. So I left, and I now spend all of my time on AI: as CEO of Fifth Turn Capital, partnering with management teams to acquire and grow established AI companies, and as CEO of Flowlinx, building AI-native software myself.
"The riskiest position in a platform shift is the comfortable one."
Story versus business
If portfolio management teaches one transferable skill, it is this: separating a durable business from an exciting story. Public markets run on stories too — every cycle has its darlings — but two decades of being graded on outcomes teaches you to ask the unglamorous questions underneath the narrative. Who actually pays for this? What does it cost to deliver? What happens to the economics at scale? What would have to be true for this to still matter in ten years?
AI investing needs those questions badly, because the stories have never been better. It has never been easier to build something that demos brilliantly, and never harder to tell — from the outside — whether there is a business underneath. Discipline is not the enemy of enthusiasm here; it is what makes enthusiasm useful.
How this shapes Fifth Turn Capital
At Fifth Turn Capital, that discipline shows up in practice, not in a slogan. We keep a focused portfolio rather than an index of the hype. We evaluate management teams on their rate of learning and their honesty about the technology's limits, not just their headline metrics. And we treat every investment the way a portfolio manager treats a position: with a clear thesis, an understanding of what would prove it wrong, and the patience to let a durable business compound. You can read more about how the firm works on our about page.
The operator's other half
There is one thing twenty years of managing portfolios could not give me: the view from inside an AI company. Leading Flowlinx, which builds AI-native software for real business workflows, supplies that missing half. I deal firsthand with the questions every AI leadership team faces — reliability, pricing, trust in a probabilistic product — and that experience sharpens every investment judgment I make. The investor in me knows how to weigh a business; the operator in me knows how it feels to build one. AI investing rewards people who can do both.
A closing thought
I sometimes describe this chapter as the same career, brought closer to the businesses I study. For twenty years I analyzed companies from the outside. Now I work alongside management teams inside established AI companies — applying the same discipline, closer in, where it can shape the outcome instead of just measuring it. If you are an owner or operator building at this frontier, that is exactly the conversation I want to have.
