Jeannie Cahalane
Underwriting is where commercial real estate decisions are made and where judgment most often breaks down. Former architect turned investor Steven Song built Diald to fix this cognitive bottleneck.
1. You come from architecture and investing rather than software. When did it click that the bottleneck wasn’t access to data but making sense of messy signals?
Steven Song: It became obvious after years of watching deals succeed or fail for reasons that never showed up in a spreadsheet. The data was never the problem. Humans simply can’t process the sheer complexity of real-world conditions around a site — politics, safety, sentiment, zoning nuance — at any meaningful scale.
Once you see that, the conclusion is unavoidable: CRE doesn’t need more data. It needs intelligence. So I built Diald to turn chaotic, qualitative noise into structured, reliable reasoning. If you can solve that, you unlock an entirely new era of decision-making.
2. What pain at Axle convinced you the market needed an AI decision layer, not another data source?
Steven Song: At my real estate investment firm Axle Companies, underwriting felt like flying with outdated instruments. Too much manual interpretation. Too much variability across analysts. And far too slow.
We didn’t need more inputs — we needed a system that could think. A system that could take thousands of weak signals and synthesize them into a coherent view. That’s the missing layer in CRE. Once you realize that, everything else is downstream.
3. Proptech is crowded with sourcing and CRM tools. Why focus on underwriting?
Steven Song: Underwriting is where conviction is created. It’s the core of the entire CRE engine. And ironically, it’s the part everyone ignored because it’s the hardest.
Most proptech went after the surface layer — sourcing, CRM — because it’s easier. But if you want to transform the industry, you go after the bottleneck: the decision itself. That’s underwriting. That’s where real leverage is.
4. What structural bottlenecks does Diald attack first?
Steven Song: Three fundamental ones:
- Cognitive overload — humans aren’t built to process thousands of unstructured signals per site.
- Inconsistency — the same deal gets different answers depending on who looks at it.
- Speed — underwriting cycles are far too slow for a modern market.
Diald removes those constraints.
5. Diald uses multiple specialized AI agents. How do you make it feel more like a transparent analyst team than a black box?
Steven Song: A single general model is like asking one person to master every discipline in real estate. Not realistic.
We built Diald with specialized agents — zoning, sentiment, safety, market context — each optimized for its domain. They report their reasoning, and then a coordinator agent synthesizes it. It works exactly like a high-performing analyst team.
Transparency isn’t an afterthought; it’s built into the architecture.
6. What’s been most challenging about teaching AI to reason over soft factors like politics, zoning nuance, or neighborhood history?
Steven Song: Soft signals are noisy. They shift. They contradict each other. But they’re also where most deals are won or lost.
The challenge is teaching AI to think probabilistically — to weigh patterns rather than fixate on single datapoints. We solved it by training agents to benchmark conditions against thousands of comparable environments. Relative reasoning, not absolute claims. That’s how experienced humans think.
7. Is Diald a power tool for analysts or a future infrastructure layer?
Steven Song: Both — but the endgame is infrastructure.
Right now, it’s the most powerful analyst you can hire. Over time, it becomes the intelligence layer that sits underneath acquisitions, lending, asset management — everything. Underwriting is the foundation. If you control that layer, you influence the entire CRE workflow stack.
8. How will AI shift roles and value boundaries across developers, LPs/GPs, brokers, and analysts?
Steven Song: AI will compress the industry’s cognitive hierarchy.
- Analysts will spend less time gathering facts and more time validating strategy.
- Brokers will be armed with instant, data-driven narratives instead of intuition.
- Developers and GPs will differentiate on decision speed and clarity, not headcount.
- LPs will expect AI-driven transparency as the baseline.
The net effect: smaller teams, faster cycles, higher-quality decisions.
9. What do investors and clients still underestimate about AI in underwriting?
Steven Song: Most people underestimate how much underwriting is pattern recognition, not math. They also underestimate how inconsistent human judgment is — and how quickly AI can normalize that.
AI isn’t just automating tasks; it’s upgrading the fundamental decision mechanism. Once people see that, the adoption curve steepens dramatically.
10. How do you think about network effects — number of properties seen vs. depth of decisions tied to outcomes?
Steven Song: Depth beats volume.
It’s far more valuable for the system to deeply understand decisions and outcomes than to skim tens of thousands of shallow examples. But the real power comes from combining both: deep reasoning across a large surface area.
That’s when the intelligence curve becomes exponential.
11. What does success look like for Diald in five to ten years?
Steven Song: Diald becomes the default qualitative underwriting engine for global CRE. Not a tool — an expectation. Underwriting becomes faster, more accurate, and less dependent on who happens to be in the room.
If a small team in a secondary market can underwrite with the same clarity as a major GP in New York or Seoul, we’ve succeeded. We’re democratizing high-level judgment.
12. What’s the most common strategic mistake founders make when building AI in traditional industries?
Steven Song: They start with data instead of physics — the underlying mechanics of the workflow.
Most founders build dashboards. They chase APIs. They try to “add AI.” That doesn’t work. You have to rebuild the workflow from first principles and then place AI where human cognition breaks down.
Don’t build AI to show off AI. Build AI that makes the fundamental bottleneck disappear.
Editor’s Note
This interview examines how AI decision intelligence can standardize judgment and reduce inconsistency in commercial real estate underwriting.
