Simon Glairy has spent the last decade at the intersection of risk and technology, witnessing firsthand the slow, often painful digital transformation of the insurance industry. As a leading expert in Insurtech, he has become a vocal advocate for replacing outdated physical inspection models with high-precision data analytics. His insights into the “coverage gap” have made him a sought-after voice for understanding how multi-family housing and condominium associations can finally find fair pricing in a volatile market. Today, we sit down with him to discuss the evolution of commercial property insurance and the massive technological shift currently reshaping the landscape.
The following discussion explores the systemic failures of traditional insurance carriers in the commercial real estate sector and the data-driven solutions emerging to solve them. We delve into the mechanics of individual building underwriting, the rising impact of catastrophe losses on global premiums, and the strategic divide between AI-native startups and legacy institutions attempting to modernize.
For years, a massive segment of the commercial real estate market has been essentially ignored or mispriced by traditional insurers. How do you define this specific “coverage gap,” and why has it remained such a persistent problem for apartment buildings and condo associations?
The gap exists because traditional carriers treat these properties like a “middle child” that doesn’t fit into any pre-defined box. On one hand, you have millions of apartment buildings and condo associations that are far too complex for simple personal lines or standard homeowners’ products, yet they are too small and numerous for the massive commercial programs that institutional landlords use. This creates a situation where properties are frequently declined or slapped with inadequate coverage because the legacy carrier is relying on broad, sweeping guidelines rather than looking at the building itself. It is a massive problem when you consider that these buildings house a huge portion of the population, yet they are often punished just for being a well-maintained 1960s block that doesn’t meet a rigid, “one-size-fits-all” condition guideline. The result is a market full of frustrated property owners who are paying for the perceived risks of their “class” rather than the actual risks of their specific brick-and-mortar reality.
You have often argued that the lack of coverage in this sector isn’t a “risk problem” but rather a “technology deficit.” How does moving toward a model that ingests hundreds of data points change the actual outcome for a property owner?
When we talk about a technology deficit, we are really talking about a lack of vision in how data is utilized to see the truth of a property. Instead of a guy with a clipboard walking around a roof once every few years, an AI-native platform can ingest hundreds of structured and unstructured data points—everything from high-resolution aerial imagery and geospatial info to environmental metrics and historical performance. This allows for a granular risk profile that simply didn’t exist five years ago, where a 210-person operation can manage a massive portfolio without the slow, manual drag of physical inspections. For the property owner, this means the difference between a flat rejection and a fair price, because the system recognizes the actual characteristics of the building in real-time. It’s about replacing the “gut feeling” of a legacy underwriter with a proprietary model that treats every property as an individual, which is the only way to achieve truly competitive and fair terms in today’s environment.
With the recent news of a $40 million Series C funding round and a total of $95 million in capital raised, what does this level of investor interest tell us about the current state of the commercial property market?
This influx of capital, especially coming from heavy hitters like Zeev Ventures and even figures like former Super Bowl champ Harris Barton, is a resounding signal that the market is ready for a category leader. We are seeing a firm that exited 2025 with $275 million in gross written premium and now manages over $100 billion in insured assets, which proves that this isn’t just a speculative bet anymore—it’s a proven, scalable model. Investors are looking at the lean operations of technology-native insurers and realizing they can cover 65% of the U.S. population across more than 20 states with far more efficiency than a giant legacy firm. The fact that this latest round was completed at a higher valuation, even during a time of economic scrutiny, shows that the “growth capital” stage has arrived for those who can accurately price risk. It is a clear message to the industry: the era of the “inspector with a ladder” is being replaced by a digital-first architecture that can handle the massive scale of the American commercial landscape.
The industry is facing a significant challenge with catastrophe losses now regularly exceeding $150 billion annually. How does an AI-driven approach help navigate these rising environmental pressures compared to the methods used by legacy carriers?
The reality is that we are moving away from the $100 billion “bad year” benchmark and into a world where $150 billion in annual losses is the new, painful norm, according to the latest industry data. Legacy carriers are reacting to this by tightening their underwriting guidelines and pushing attachment points higher, which essentially just leaves more property owners out in the cold. However, an AI-driven strategy doesn’t just retreat; it prices more precisely by understanding the environmental data and the building’s specific resilience metrics. While 70.6% of organizations claim they delivered new AI underwriting tools in 2025, only about 20.4% of leaders actually feel confident in their strategy, which creates a massive opening for those who built their business on AI from day one. By using proprietary data rather than retrofitting a legacy system, a modern insurer can remain active and profitable in high-stakes environments where others are simply forced to withdraw.
There is often a fear that automation will replace the human element in insurance, yet there seems to be a strong emphasis on “enhancing agent-facing technology.” Why is the independent agent still so vital to this high-tech transformation?
Insurance will always have a human component because agents are the ones on the front lines, navigating the emotional and financial complexities for their clients. The goal of the new capital being deployed isn’t to cut the agent out, but to give them better tools so they can provide answers in minutes rather than weeks. When an agent can use a platform to instantly see a granular risk profile for an apartment block, it transforms them from a frustrated middleman into a hero for the property owner. We are seeing a deliberate choice to support the independent agent channel because they understand the local nuances that data alone might miss, but they need that data to be competitive. By focusing on agent-facing tech, we are essentially arming the brokers with the same high-resolution insights that the underwriters use, creating a much smoother, more transparent transaction for everyone involved.
Looking at the widening gap between “AI leaders” and “AI laggards”—with some reports showing leaders generating six times the shareholder returns of their peers—what is your forecast for the commercial property segment over the next five years?
My forecast is that we are going to see a rapid “sorting” of the market where the commercial property segment extends far beyond just apartments and condo associations into every corner of the built environment. Legacy carriers will likely continue to struggle with strategic uncertainty, and their lack of a clear, actionable AI strategy will cause them to lose the most profitable, well-maintained risks to the more agile, data-driven competitors. We will see the “inspected by AI” model become the industry standard, and firms that haven’t moved away from physical-first underwriting by the end of the decade will find themselves managing only the most distressed, high-risk assets that no one else wants. Ultimately, the winners will be the ones who didn’t just add AI as a “bolt-on” feature, but those who used it to fundamentally reimagine how we value and protect the places where people live and work.
