The AI revolution is shifting from broad, general-purpose models to deeply specialized, high-performance AI systems tailored for specific industries. While much of AI’s early adoption has focused on automation and efficiency gains, the next wave of transformation is far more profound. AI is now capable of capturing, structuring, and productizing industry-specific tribal knowledge, enabling entirely new ways of working that were never possible before.
Tribal knowledge refers to unwritten, informal, and often experience-based knowledge that is known within a group, organization, or community but is not documented. It is typically passed down through word of mouth, observation, or practice rather than formal training or written procedures.
For example, a seasoned factory technician understands that a particular piece of equipment tends to overheat after a few hours and has developed a workaround to prevent downtime. An experienced medicinal chemist knows, through years of trial and error, what types of chemical compositions could lead to failures during drug development, and instinctively avoids structural modifications that, while theoretically promising, are likely to trigger solubility issues or metabolic instability. A veteran telecom network engineer knows how seasonal temperature changes affect fiber optic cable expansion and signal attenuation, leading to performance failures, and proactively adjusts power levels or reroutes traffic before issues occur.
Historically, this kind of expertise was difficult to transfer, standardize, or scale, creating bottlenecks in industries reliant on human experience. AI is now breaking those constraints, turning highly specialized, expert-driven processes into AI-powered, scalable, and even autonomous workflows.
As a venture investor, my thesis focuses on identifying and backing vertical AI startups that capture and productize tribal knowledge in ways that create completely new ways of working. These startups don’t just apply general-purpose AI to existing tasks; they redefine entire industries by embedding AI into their core operations, building mission-critical infrastructure that transforms how work is done.
Why digitally capturing and productizing tribal knowledge is critical
Industries have always relied on expertise that isn’t easily documented — whether it’s a seasoned machinist adjusting a factory line based on intuition, a doctor diagnosing a rare disease based on years of pattern recognition, or a venture capitalist recognizing a great founder before anyone else does. This knowledge is mission-critical, yet fragile — when experts retire, switch jobs, or leave, their insights often disappear with them.
In today’s volatile macroeconomic environment, where labor shortages, supply chain disruptions, and rapid technological change are straining industries, business continuity and industry resilience depend on digitizing and operationalizing this knowledge. AI-enabled tribal knowledge capture ensures that expertise is preserved, scaled, and continuously improved upon, reducing dependency on individual workers and enabling more adaptive, responsive industries.
Moreover, productizing tribal knowledge — turning it into scalable AI-driven decision-making systems, automated workflows, and industry-specific AI models — does more than just protect institutional knowledge. It creates powerful new business models, enabling companies to monetize expertise, build AI-powered industry platforms, and embed themselves as indispensable infrastructure.
Enter: Vertical AI
For years, AI development has been dominated by horizontal AI — broad, general-purpose models that work across industries. These systems, like GPT-based chatbots or general image recognition tools, are designed to handle a variety of use cases. While useful, they often lack the deep industry expertise required to outperform human professionals in highly specialized, high-stakes workflows.
By contrast, vertical AI is purpose-built for a single industry, trained on proprietary datasets, and fine-tuned with tribal knowledge that gives experts their competitive edge. Instead of providing generic intelligence, vertical AI deeply understands industry nuances, domain-specific workflows, and real-world operational constraints.
The difference is not just in efficiency gains — it’s in creating entirely new capabilities that were previously impossible. This drives a fundamental transformation in how industries operate. Vertical AI doesn’t just enhance existing workflows; it enables a complete reinvention of work itself.
How AI captures tribal knowledge: a multi-modal approach
AI’s ability to capture tribal knowledge at scale is being driven by multi-modal AI systems that combine vision, voice, text, IoT data, and predictive analytics to learn from human experts in real time. Unlike traditional knowledge management systems, which rely on static documentation, AI can actively observe, extract, and refine expertise dynamically. Some examples include:
Healthcare: AI-powered speech recognition captures doctor-patient interactions, learning how top doctors diagnose complex conditions. Over time, it fine-tunes medical diagnostic AI models, reducing errors and improving patient outcomes.
Manufacturing: Computer vision AI observes factory workers adjusting machinery settings based on subtle vibrations and wear patterns. It then automates predictive maintenance, reducing downtime and increasing efficiency.
Construction: AI-powered smart wearables track how senior engineers evaluate site risks, capturing this expertise and embedding it into real-time risk assessment tools for field workers.
Finance & venture capital: AI listens to investment committee meetings, analyzing how top investors make decisions. It then trains a proprietary model that improves investment decisions based on historical deal outcomes.
These capabilities allow AI to go beyond just recording information — it enables machine learning models to absorb expertise, refine insights, and ultimately make high-quality decisions autonomously.
From knowledge capture to productization
Capturing tribal knowledge with AI is only the first step — the real transformation happens when that knowledge is productized into AI-driven workflows, high-performance models, or fully autonomous systems. Many AI vendors struggle to cross this chasm, as capturing industry-specific expertise does not automatically translate into a scalable, monetizable, and defensible AI product. To bridge this gap, AI vendors must move beyond knowledge aggregation and build systems that actively apply, refine, and automate decision-making — turning captured expertise into intelligent, adaptive, and industry-changing solutions.
The first step in crossing this chasm is structuring and contextualizing captured knowledge. Raw data, even when collected from the best industry experts, lacks the structure and context needed for real-world application. This can be achieved by fine-tuning foundation models with domain-specific expertise, leveraging graph-based AI (such as knowledge graphs) to represent relationships and patterns in tribal knowledge, and implementing context-aware retrieval techniques like retrieval-augmented generation (RAG) to enable AI models to dynamically pull relevant insights rather than relying on static knowledge storage.
For example, in aviation maintenance, airlines rely on the tacit knowledge of experienced technicians who can diagnose mechanical issues based on subtle signs like engine sounds, vibrations, or wear patterns. By capturing this tribal knowledge, an AI system could build a knowledge graph linking historical repair logs, technician notes, and failure patterns. With retrieval-augmented generation (RAG), the AI could then provide real-time troubleshooting recommendations, ensuring even less experienced mechanics have access to decades of institutional expertise when diagnosing aircraft issues.
At that point, the structured, contextualized knowledge needs to be converted into high-performing AI models that drive decision-making and automation. This requires implementing feedback loops to refine AI recommendations based on real-world expert validation, converting unstructured knowledge into structured decision trees or reinforcement learning-enhanced models (RLHF), and fine-tuning multi-modal AI systems.
For example, in air traffic control, experienced controllers rely on years of intuition and pattern recognition to anticipate congestion, reroute flights, and manage unexpected disruptions like weather delays or emergency landings. An AI system could capture this tribal knowledge by analyzing historical flight patterns, airspace congestion reports, and real-time controller decisions. Through feedback loops, the AI could learn from expert interventions, continuously refining its recommendations. By converting these insights into structured decision trees or RLHF-enhanced models, the AI could evolve from simply suggesting flight adjustments to autonomously optimizing air traffic flow, rerouting planes in real time, and minimizing delays across entire aviation networks.
Once the AI is capable of making industry-specific decisions, vendors must ensure it can execute those decisions autonomously. This is where AI transitions from a decision-support system to an agentic AI capable of automating multi-step workflows. To fully cross the chasm, AI vendors must embed their systems into mission-critical industry infrastructure, ensuring their AI becomes an essential part of day-to-day operations rather than an optional tool. In doing so, a vertical AI startup in manufacturing that initially offers predictive maintenance insights can transform into a self-operating AI that directly controls factory machinery, preventing downtime before it happens.
By progressing from knowledge capture to automation and full AI-driven execution, vendors turn AI from a static source of insights into an autonomous system that operates at a scale and speed beyond human capabilities. This is where the most valuable vertical AI companies will emerge — not as mere data aggregators, but as industry orchestrators, embedding AI deeply into the operational fabric of entire sectors.
Tribal knowledge is a critical ingredient to high-value, durable vertical AI
Vertical AI enables stronger value capture by embedding tribal knowledge into industry workflows
Unlike general-purpose AI models that can be easily replicated, vertical AI startups that embed captured tribal knowledge into their systems build deep, enduring competitive moats. The most valuable AI systems are those that do more than process data — they operationalize expertise, integrating decades of human intuition, learned patterns, and industry-specific heuristics into decision-making models that become essential to industry workflows. The result is an AI product that is not easily interchangeable, as it represents the accumulated experience of the industry’s top experts, distilled into a digital system that continuously improves.
For example, in manufacturing, AI-powered predictive maintenance tools learn from veteran technicians who have developed a sixth sense for identifying machine failures before they happen. Over time, the AI captures and refines this tacit knowledge, turning what was once an undocumented skillset into a systematized intelligence layer that ensures continuous uptime and efficiency. In agriculture, AI-driven precision farming doesn’t just process soil data — it learns from multi-generational farmers who have developed a deep intuition about how weather patterns, soil conditions, and crop rotations interact, then embeds that knowledge into autonomous farming systems. These AI solutions don’t just offer efficiency improvements — they become indispensable infrastructure that encapsulates decades of experience into a scalable, automated form.
This deep integration gives vertical AI companies significant pricing power and higher value capture potential. Unlike horizontal AI, which risks being commoditized, vertical AI becomes an essential part of industry operations, allowing companies to move beyond traditional SaaS pricing models into high-margin, value-based revenue structures such as transaction-based fees, outcome-based pricing, or fully AI-managed services. The deeper AI embeds itself into industry workflows, and the more tribal knowledge it can capture and productize, the more customers depend on it, making replacement nearly impossible and significantly enhancing the economic upside for AI companies.
AI-driven data network effects strengthen defensibility by continuously refining tribal knowledge
One of the strongest competitive advantages of AI-first industry disruptors is their ability to turn captured tribal knowledge into an ever-evolving, continuously improving intelligence engine. Unlike human expertise, which is finite and tied to individuals, AI learns from every interaction, continuously refining and optimizing its decision-making processes. The more industry professionals use a vertical AI system, the better it gets at replicating and even surpassing expert intuition — creating an ever-expanding data advantage that becomes nearly impossible for competitors to replicate.
For example, in construction project management, AI systems are trained on how veteran project managers anticipate delays, sequence work crews, and adapt to changing site conditions. Over time, as AI integrates real-world feedback, it refines its ability to make these decisions autonomously, becoming smarter and more reliable with each project it manages. In legal AI, contract analysis systems don’t just process documents — they learn from thousands of negotiations across firms, capturing unwritten negotiation tactics and risk-assessment heuristics that even junior attorneys struggle to acquire. This creates a self-reinforcing flywheel, where the AI model continuously builds upon the industry’s collective knowledge base, making it an essential tool for firms that want to leverage the latest and best insights without relying solely on human expertise.
This continuous learning cycle makes new entrants increasingly uncompetitive. Any company attempting to build a competing AI system would not only need to replicate years of accumulated industry knowledge, but also develop a mechanism for real-time learning and adaptation — a task that becomes exponentially harder as the leading vertical AI models pull further ahead. Over time, these AI systems become not just valuable, but indispensable, as industries come to rely on them for decisions that were once the exclusive domain of seasoned professionals.
By embedding tribal knowledge into dynamic, self-improving AI models, vertical AI companies create long-term competitive moats that extend beyond mere data collection. They turn industry expertise into a defensible, scalable, and highly monetizable asset, ensuring that their AI remains the backbone of industry operations for years to come.
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AI is no longer just a tool — it is becoming the core infrastructure that industries run on. The companies that successfully capture and productize tribal knowledge into deeply embedded AI systems will shape the next generation of industry leaders.
As an investor, I am actively looking for vertical AI startups that don’t just automate tasks, but redefine industries. If you’re building the future of AI-driven industry transformation, let’s connect.
