Dear friends, colleagues, and mentors,

Before the summer begins - and before the wave of IPOs that will, as always, send some noise through our markets, I want to set the current world aside and write about something further out: what real estate intelligence could become?

So treat this as a thought experiment. A clear-eyed piece of imagination about where machine understanding of real estate might ultimately go, written in the spirit of a research note rather than a product update.

Here is the question underneath it.

Usually, software treats a building as a fixed thing. A record. A rent roll. A valuation. A row in a spreadsheet. But no asset is fixed. A warehouse is shaped by its power capacity, its access to transport, the potential automation inside it, the zoning around it, and the tax and regulatory regime above it. An office is shaped by tenant demand, financing conditions, and the slow rewriting of how cities use space. Change any one of those, and the asset changes with it.

So what would it take for a machine to understand real estate not as a snapshot, but as a system that moves?

Building in Silicon Valley can feel like living inside a bubble.

Over time, it’s easy to forget that the rest of the world moves at a different pace — that what feels obvious here can feel abstract, even distant elsewhere. And that the growing gap in understanding technology is not just real, but increasingly a barrier to adoption.

A few weeks ago, I was traveling outside the U.S. I sat down with a friend — an experienced real estate investor, thoughtful, curious, and open to learning. We spent about twenty minutes discussing what we’re building and how it might support the industry.

Then he paused and asked:

“What is a token?”

It was a simple question. And a genuine one.

It stayed with me — not just in that moment, but later that evening, and on the 11-hour flight back to Silicon Valley.

Because beneath that question is something deeper:

A widening gap between how technology is built, and how industries understand, adopt, and trust it.

A gap we need to close — if we want progress to be shared, not siloed.

A short note before we begin

Real estate investing has always involved forming judgment under uncertainty.

Over time, valuation frameworks emerge — shaped by experience, market cycles, and the collective memory of past decisions. As new data and analytical tools evolve, I have become increasingly curious about how these familiar approaches might also change.

This letter shares a personal perspective informed by recent academic research, industry observation, and the ongoing process of learning while building technology for the investment community.

These reflections are also shaping how we are thinking about product design at Reml — and we look forward to sharing more tangible examples in the coming months.

Welcome to The Reml Letter — a monthly note where I explore emerging technology and its real-world impact on investment decision-making.

This month, I’d like to explore why AI won’t replace analysts — and how it will redesign workflows in institutional real estate investing.

Welcome to The Reml Letter — a monthly note where I explore emerging technology and its real-world impact on investment decision-making.

AI is not a finished product; it’s a learning journey. The technology is evolving quickly, and so is how it’s being applied. Through this letter, I invite you to learn alongside me — through observations shaped by building at the intersection of technology and industry.

I write from a dual vantage point: eight years in institutional investing, and now, building an AI company (Reml) in Silicon Valley. This letter is my attempt to bridge those two worlds — translating fast-moving technological advances into practical insight for an industry where decisions are high-stakes and judgment matters.

For this first issue, at the opening of 2026, I’d like to explore the current state of AI agents — and what effective, responsible deployment looks like in practice for institutional investors.