The number of AI products on the market increases daily, and with each new entrant comes the same question: what’s its value, and how does it differ from everything else?

While today’s ready access to foundation models, generalized agents, and orchestration stacks may indicate that the verticalized AI market is becoming commoditized, a deeper look into the AI market suggests otherwise. Just as in the early Cloud Era, when anyone could rent computing power to build their products, today, anyone can access AI foundation models. This similarity suggests that, also similar to the Cloud Era, the winners of the AI Era will not be defined by those who try to go up against the core providers (e.g. Google Cloud or Azure for the Cloud, OpenAI or Anthropic for LLMs), but by those who build AI systems on top of the core providers to not just function, but continuously improve, embed themselves into workflows, and become highly defensible over time.

As early-stage investors in AI-native companies, Glasswing Ventures constructs its portfolio around these characteristics. We look for founders building products with strong AI moats, meaning the systems leverage proprietary data to operate within specific verticals, learn via the context of their applications, respond to feedback in real time, and become so customized that they eventually become the workflows themselves.

The edge: unique access to proprietary data

Before considering the characteristics that form the moat itself, we must look to the edge from which the moat evolves. In AI, this edge is proprietary, irreproducible, and permissioned data sourced exclusively from customers.

Proprietary data is a competitive edge because it captures the context in which decisions are made. It reflects the unique fingerprints of an enterprise and signals how organizations operate, including their incentives, constraints, and outcomes. Additionally, because of its uniqueness, proprietary data cannot be scraped, bought, or synthetically generated at scale, making the product difficult to replicate.

Giving AI access to proprietary data is what drives a powerful cycle of verticalization, reinforcement learning, strong architecture, and intelligent human collaboration that together propel AI systems to become integral components of their industries and irreplaceable by competitors.

Four critical forces define a lasting AI moat

Within a defensible AI system, four key forces leverage proprietary data to create continuous, dynamic momentum. Verticalization, reinforcement learning, architecture, and collaborative intelligence work in parallel to build an impassible barrier against competitors.

1: Verticalization sharpens AI’s edge

Automating a generic task isn’t unique, but automating a task that fits within the context of the messy, human, exception-filled, regulation-bound complexity of a specific industry is. Vertical AI products, AI products that are purposefully built for the industry they serve, are not just accurate in isolation. They are accurate within the unique workflow in which they operate. They understand the constraints, compliance rules, odd data formats, and tribal knowledge that may only live in someone’s inbox or a spreadsheet named “final_v9_ACTUAL_final.xlsx.” When such domain knowledge is encoded in agent behavior, trust, fit, and human alignment follow. As a result, vertical AI products become woven into workflows, acting as a colleague to humans rather than just another tool to learn how to use.

The success of verticalization in AI systems is measured by the accuracy of real-world performance against objectives that matter in context: throughput, revenue impact, and defect prevention, for example. Because vertical AI products work within real-world situations, they are not set-and-forget products. Rather, they must leverage reinforcement learning to operate as continuous, always evolving, real-world loops, bringing us to Force #2.

2: Reinforcement learning builds momentum

AI systems create a lasting advantage when they are able to remember and learn from each interaction they have in the real world. If an AI system is built with reinforcement learning as part of its training, then it retains all of the task-specific, high-signal feedback it receives. As the AI internalizes each correction, override, success, and failure, it continuously improves. It begins to understand the nuances of how a business operates, and with this highly specific knowledge, evolves from a tool to a workflow.

When working together, verticalization and reinforcement learning enable the AI system to cater to the unique mistakes, recoveries, and downstream outcomes that happen at every business. Yet, neither can happen without the right architecture in place, bringing us to Force #3.

3: Architecture shapes advantage

In the SaaS era, people often experience the benefits of software applications without recognizing the underlying platforms or frameworks that make them function. Similarly, in the AI era, people often look only at the surface-level effects of AI without exploring the product’s underlying architecture, which is where the real durability takes shape.

Architecture refers to the orchestration of models, agents, memory, tools, and decision logic that shapes how data is processed. While two AI systems may use the same models, they will deliver radically different outcomes with different architectures in place.

A strong architecture doesn’t reveal itself in a polished demo, but its strength becomes clear when things inevitably go wrong, such as when an agent forgets, a tool chain fails, or a plan needs to be rethought.

MIT research echoes this sentiment, finding that while most agents can impress humans in controlled settings, they fail to succeed when deployed at scale. In complex real-world environments, the systems that can recover, adapt, and self-correct amidst moments of failure are the ones that endure. This resiliency further enables AI products to become embedded in a business’s workflow, bringing us to the next phase of the cycle.

4: Collaborative intelligence creates stickiness

Even the most advanced AI products are disposable if they cannot be embedded into the daily workflows of a business. Durable AI systems blend classical machine learning, rules-based logic, generative models, and domain heuristics to operate as the nervous system of enterprise operations.

AI products that achieve this level of integration weave into decision cycles, approvals, audits, and daily routines, working alongside humans to generate business value. Collaborative intelligence platforms leverage proprietary data, reinforcement learning, and strong architectures to decompose problems, apply context, and enforce correctness. These AI systems result in dashboards being bookmarked, CRM notes being routed automatically, and escalations or approvals being based on their outputs. With collaborative intelligence, AI systems create behavioral lock-in and become stronger with every run, making removing them not just a technical lift but a business risk.

The Glasswing Perspective

With proprietary data as the edge, verticalization, reinforcement learning, architecture, and collaborative intelligence compound to create lasting differentiation in AI systems. While access to proprietary data limits competitors’ ability to copy, verticalization encodes domain mastery, reinforcement learning turns usage into private learning signals, architecture ensures resilience in the face of complexity, and collaborative intelligence makes AI habitual, trustworthy, and indispensable.

In enterprise environments where stability is vital, an AI product that harnesses all four forces ceases to be a tool and becomes an efficient co-worker, driving tremendous business value for enterprises.

Are you building a defensible AI product? Tell us about it at info@glasswing.vc.