NU DMZ, LLC

08/07/2026

One of the biggest AI mistakes I see is starting with the tool instead of the workflow.

That is especially true in product management and real estate investing.

The better question is not:

“Where can we use AI?”

It is:

“Where are we losing the most time, consistency, or decision quality today?”

In a product environment, that might be synthesizing customer feedback, drafting requirements, comparing competing priorities, or preparing stakeholder updates.

In real estate, it could be reviewing listings, organizing comparable properties, stress-testing assumptions, or identifying which deals deserve deeper analysis.

The practical value of AI comes from removing friction around repeatable work so more attention can go toward judgment.

A simple framework I like:

• Find the repetitive step.
• Define what “good” output looks like.
• Use AI to accelerate the first pass.
• Verify the result against real data and context.
• Improve the process based on what consistently works.

The goal is not to automate every decision.

It is to create more time for the decisions that actually require experience, tradeoffs, and accountability.

That is where I think the strongest AI use cases will continue to emerge—not from replacing expertise, but from giving experts more leverage.

What is one part of your workflow you think AI should handle better than it does today?

08/06/2026

A spreadsheet tells you what you decided.

A decision log tells you why.

That distinction matters more than it seems—especially in real estate investing, AI, and product management.

When evaluating a property, it is easy to record the purchase price, projected rent, expenses, and expected return. Months later, however, the harder questions are:

Why did I trust that rent estimate?

Which risks did I consider acceptable?

What information would have changed my decision?

Product teams face the same challenge. A roadmap captures what was prioritized, but not always the customer evidence, tradeoffs, and assumptions behind the decision.

This is one area where AI can be genuinely useful.

Instead of asking AI to make the decision, use it to help structure the reasoning:

State the decision being considered.
Document the evidence supporting it.
Identify the assumptions that remain uncertain.
Record the alternatives that were rejected.
Define what results would trigger a reassessment.

This creates something more valuable than a one-time answer: a repeatable learning system.

Over time, decision logs can reveal patterns in how we evaluate opportunities, where our assumptions are consistently accurate, and where bias may be influencing our judgment.

Good decisions still require experience.

But documenting the reasoning makes that experience easier to improve.

Do you document the reasoning behind major decisions—or mostly record the final outcome?

08/04/2026

Bad assumptions can compound faster than good returns.

That is true when underwriting a rental property, launching a technology product, or using AI to support a major decision.

A deal may appear profitable because the projected rent is optimistic, maintenance is underestimated, or vacancy is ignored.

A product may appear promising because the team assumes customers will change their behavior, adopt a new workflow, or pay for a feature without enough evidence.

AI can make both analyses faster—but speed does not make the assumptions more accurate.

A practical approach I use is to pressure-test the downside before becoming attached to the upside:

• What happens if revenue or rent is 10% lower?
• What happens if expenses are 15% higher?
• Which assumption has the greatest effect on the outcome?
• What evidence would prove the original thesis wrong?
• Is there enough margin for error if several things go against the plan?

This is where product management and real estate investing overlap.

Both require disciplined prioritization, measurable assumptions, and the willingness to walk away when the evidence no longer supports the opportunity.

AI is especially useful for comparing scenarios, organizing data, and identifying questions that deserve deeper investigation.

But the final advantage still comes from judgment: knowing which assumptions to trust, which ones to verify, and which risks are not worth taking.

What assumption do you pressure-test first when evaluating a new investment, product, or business opportunity?

08/02/2026

Most AI projects don’t fail because the technology is weak.

They fail because no one clearly defined the decision the technology was supposed to improve.

That lesson applies equally to product management and real estate investing.

Before using AI, I try to identify the actual bottleneck:

Is the problem slow research?

Inconsistent analysis?

Too much unstructured information?

Or a decision process that depends too heavily on memory and instinct?

In real estate, AI can help organize property data, compare financing scenarios, summarize market information, and flag assumptions that deserve a closer look.

In product management, it can help synthesize customer feedback, identify recurring themes, draft requirements, and accelerate early analysis.

But adding AI to a weak process usually creates a faster weak process.

A more practical approach:

Define the decision that needs to be made.
Document the inputs required to make it well.
Identify the repetitive work slowing the process down.
Use AI to support that work.
Keep human judgment accountable for the final outcome.

The competitive advantage is not simply having access to AI.

It is knowing where AI improves the workflow—and where experience, context, and judgment still matter more.

Where have you seen AI create real value, rather than just adding another tool to the process?

12/14/2023

NU DMZ, LLC

12/10/2023

12/07/2023

https://www.boston.com/real-estate/home-buying/2023/12/06/two-mass-cities-among-hottest-housing-markets/

🤑

Worcester and Springfield are among the top 10 housing markets in the country for 2024 according to Realtor.com.

12/04/2023

So good. Pho-real

12/02/2023

Sushi time