At first glance, working in AI software might seem worlds apart from managing tenants, pitching VCs, or building a sustainable fashion app. But what I’ve learned working in tech — especially at the intersection of AI and enterprise software — has deeply shaped the way I approach everything else I do.
Whether I’m dealing with a tenant issue, iterating on an MVP, or evaluating early-stage startups in venture capital, I find myself drawing from the same toolkit: systems thinking, data-driven intuition, and an obsession with scalability.
Here’s how working in AI software has sharpened my edge across all areas of my portfolio life.
1. Managing a Rental Property: Think Like a Product Manager
Owning a rental in New York at 24 is no passive side hustle — it’s an operational beast. But my background in software taught me to treat it like I would any B2B product:
- Tenants are users — they need clear onboarding, reliable support, and consistent service.
- The unit is an asset — like software, it requires regular updates (maintenance), UX improvements (repairs/upgrades), and performance monitoring (cash flow tracking).
- Recurring issues = feature bugs — if the same problem keeps popping up, it’s a signal that something in the system is broken, not just bad luck.
Instead of reacting emotionally when something goes wrong, I started building processes — automated rent collection, Google Forms for maintenance requests, a Notion-based dashboard for managing vendors and costs. AI trains you to look for inefficiencies and automate repeat tasks. That mindset keeps me sane and keeps the property profitable.
2. Working in Venture Capital: Pattern Recognition and Prompt Engineering
In VC, people talk about “pattern recognition” as a skill — seeing repeat signals in founders, markets, or product timing. That’s literally what AI is trained to do. My work in software has taught me to:
- Zoom out and map macro-patterns.
- Zoom in and analyze micro-signals (burn rate vs. traction, founder-market fit, unit economics).
- Ask better questions — and ask them early.
When you work with AI, you realize that what you feed the system matters just as much as what it outputs. In venture, that translates to being intentional about the inputs you gather: founder questions, customer feedback, industry research. Garbage in, garbage out applies in both contexts.
Plus, being in AI makes you more skeptical of hype. Everyone’s pitching “AI-powered” something — but working in this space means I can smell the difference between a fine-tuned transformer model and a Zapier hack.
3. Starting a Fashion App: Building Systems for Human Creativity
My fashion tech app isn’t just about clothes — it’s about helping people feel better in what they already own, and making fashion more social, personal, and sustainable. But building it would be impossible without a deep understanding of how AI systems work — and where their limitations lie.
Here’s what working in AI taught me about building in fashion tech:
- Users need guidance, not just tools. An app that tells someone “wear these jeans with this top” without understanding their context or taste will flop. The secret is in blending algorithmic suggestion with human nuance — like AI stylists that learn with you, not just for you.
- AI ≠ magic. Our MVP started simple: scan your closet, get outfit ideas, share looks with friends. But even that requires thoughtful data structuring, clean UX, and decision logic that doesn’t confuse or overwhelm. Working in AI made me realistic about technical feasibility and MVP trade-offs.
- The feedback loop is everything. Just like an LLM improves with fine-tuning, a fashion app improves as users tag, wear, and share outfits. Designing this feedback loop — and making it enjoyable — is both art and science.
4. Scaling Across Spaces: Thinking in Layers
The biggest thing AI has taught me is how to think in layers. Every system — whether it’s a neural network, a rental business, a fashion app, or a venture deal — has layers:
- Surface layer: What the user sees (interface, results, brand).
- Logic layer: What drives the system (business model, incentives, algorithms).
- Infrastructure layer: What makes it work behind the scenes (data, teams, tools).
When I analyze a startup, manage my rental, or design a new product feature, I mentally peel back those layers. That’s a software habit — and it helps me see problems (and opportunities) more clearly than I used to.
5. Bonus: AI Trains You to Iterate Fast and Ship Smarter
One of the best habits I’ve picked up from AI software work is the concept of constant iteration. There is no perfect v1 — there is only testing, learning, and improving.
- With my rental, that meant refining my tenant onboarding process after each lease.
- With my fashion app, it meant testing AI outfit suggestions manually before building them into the product.
- In VC, it means refining how I evaluate founders, how I track thesis areas, and how I communicate ideas clearly.
The AI world moves fast, and it teaches you to build lean, prioritize what matters, and be okay with launching before it’s “ready.” That’s powerful everywhere.
Final Thoughts: The Unseen Threads That Connect It All
People often ask me how I juggle it all — real estate, tech, VC, MBA, and startups. The truth is, I’m not juggling. I’m weaving. Each experience builds on the other. Each role sharpens a new edge.
AI isn’t just a field I work in. It’s a way of thinking — systems-driven, feedback-informed, scalability-focused. And that mindset is what allows me to manage a property like a business, build a startup that solves real pain points, and back founders who see the future clearly.
Tech doesn’t limit you. It multiplies you.