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When I started in venture capital five years ago, Twenty Five Ventures had a simple bias. I looked for founders who could execute.
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Could they move fast? Could they hire people who raised the bar? Could they get the product into the market before a larger company woke up and crowded the trade?
For a while, that edge was enough. In software, speed covered a lot of sins. If you could build faster than everyone else, you could create real distance.
AI changed that.
Today, output is cheap. A small team can write code, spin up a design, automate support, and get a product into the market at a pace that used to require a large organization behind it. A startup can appear polished, responsive, and significantly larger than it actually is. The floor has moved up. Good enough is everywhere.
That is the part many people still miss. AI makes teams faster. It also made execution easier to imitate.
That changes the question for founders and investors.
It is no longer enough to ask who can execute. The better question is, who is hard to replace when everyone has access to the same engines of production?
That is where I now look for a different kind of moat. Not output alone, but position. By position, I mean your relative location in relation to the customer, workflow, and transaction.
I keep coming back to three forms of advantage.
Proximity.
Partnership.
Selection.
Proximity comes first.
The best founders are close to the pain. They do not study the customer from a distance. They know where the workflow breaks, where money leaks, and where a small delay creates real cost. In proptech, this matters more than most sectors. Real estate still runs on fragmented systems, local nuance, and human coordination. The pain is rarely in the headline problem. It sits in the missed handoff, the stale lead, the leasing delay, the missing document, the maintenance ticket no one owns. A building rarely has a leasing problem. It has approvals that sit untouched over a long weekend, tours that never get confirmed, and applicants who drop off because no one followed up in time.
AI helps you build a product. Proximity indicates where the product should be placed. When I talk to founders, I ask them to walk me through the last time they watched a customer do this work end-to-end and what surprised them.
Partnership is the second moat.
When output becomes easier, distribution and trust become more valuable. The model is not the business. The business is the position you hold around the model. Who owns the customer? Who controls the data? Who sits inside the workflow? Who trusts you enough to let you touch the transaction?
This is where many AI companies will stall. A clever feature is easy to copy. Embedded access is not. The real signal is not a logo on a slide. It is a champion willing to rip out a legacy process, a data-sharing agreement that took months to negotiate, or a design partner who is rewiring their workflow alongside you. I pay close attention to who inside the organization is betting their reputation on the company. In proptech, a company integrated into a property management system, mortgage workflow, brokerage stack, or transaction layer has more reach. It has a significant impact on how work gets done.
Today on new deals, I now ask three questions: how close are you to the pain, how secure is your position in the workflow, and what did you decide not to build first?
Selection is the third moat.
In a market flooded with output, judgment matters more. Knowing what to build matters. Knowing what not to build matters more.
Selection is about choosing the right customer, the right wedge, and the right workflow. Which problem is painful enough to pay for? Which buyer has a budget? Which use case creates urgency instead of polite interest? AI for property management is a slogan. Recovering lost renewals in Class B suburban assets is a wedge. I get nervous when a deck has ten use cases and no first customer who feels one of them viscerally. Most startups do not fail because they could not build enough. They fail because they built for pain that was too soft, too broad, or too disconnected from a real buyer.
This is why the line between non-agentic and agentic AI matters. Non-agentic tools sit on top of the workflow. Agentic systems aim to live inside it.
Most AI today helps teams answer, retrieve, or automate. That has value. A chatbot answers a renter’s question. RPA moves data between systems. RAG pulls context from documents and policies before generating a response. These tools enhance speed and minimize manual labor. They usually stop at the answer.
25V Research
RPA, Robotic Process Automation 😅
RAG, Retrieval-Augmented Generation 😄
Agentic systems aim to do more. They plan, act, check progress, and move work toward completion. In proptech, that could mean a leasing agent who qualifies renters, schedules tours, follows up, and hands off only when needed. Or a maintenance agent that triages issues, routes vendors, updates residents, and closes the loop.
That shift matters because real estate is not a prompt business. It is a workflow business.
AI has democratized output. It has not democratized judgment. It has not democratized trust. It has not democratized lived access to hard problems.
Five years ago, I sought founders who could outperform the market. Don’t get me wrong, I still value the hustle; I am absolutely still doing so.
But now I spend at least as much time asking whether they are closer to the pain, stronger in their partnerships, and sharper in what they choose to build.
That is where moats are being rebuilt.
Sincerely yours,
Maximillian Diez
GP, Twenty Five Ventures
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