"Palantir for X" is inevitable, but not enough
In the past, only certain industries placed significant value on having a core knowledge graph – soon every vertical will
For years, Palantir has been the benchmark for AI-powered decision intelligence, transforming fragmented enterprise data into actionable insights. But as AI advances, a new paradigm is emerging — one that extends beyond optimizing individual enterprises and instead restructures entire industries. This shift presents one of the most compelling investment opportunities in AI today.
The next generation of vertical AI winners won’t just be “Palantir for X” startups, applying Palantir’s model to new sectors. They will be something much bigger: companies that build, own, and operationalize industry-wide ontologies — intelligence layers so deeply embedded in an industry’s decision-making processes that they become irreplaceable. Unlike traditional enterprise AI solutions, which help companies make better internal decisions, these industry-wide AI platforms will define how entire sectors operate, creating shared decision-making infrastructure that competitors can’t easily displace.
A company that owns this kind of infrastructure doesn’t just sell AI software — it becomes the foundation upon which an industry runs. This structural advantage is what separates billion-dollar software companies from trillion-dollar platforms. The question is not who can build the next Palantir, but who can build something even more transformative — an intelligence layer so embedded in an industry that operating outside of it becomes unthinkable.
The real opportunity: industry-wide ontologies
Palantir’s success came from a simple but powerful insight: data alone is not enough. To make real-world decisions, businesses need structured and contextualized intelligence — an ontology that maps how data, workflows, and decision-making processes interconnect in real-world practice. This approach has been transformative at the enterprise level, enabling high-stakes decision-making across defense, healthcare, energy, and finance.
But the next wave of AI isn’t just about helping individual enterprises organize their own operations. It’s about structuring entire industries. Instead of each company building its own decision intelligence in isolation, AI-driven ontologies will emerge as shared infrastructure, standardizing how industries process information, automate workflows, and make critical decisions.
Consider the financial sector, where Bloomberg Terminal has long been the default infrastructure for real-time financial data. It didn’t just provide better tools for individual firms — it defined the standard way the entire industry consumed market information. In the future, an AI-powered ontology for the financial services vertical won’t just provide predictive insights for banks and insurers; it will set the framework for how financial risk is modeled, how regulatory compliance is enforced, and how transactions are optimized. The most valuable AI companies of the future won’t just sell decision-support software; they will establish the de facto operating system of an entire industry.
The real breakthrough today is that AI is making it possible to create these industry-defining intelligence layers in every vertical — not just the most lucrative segments of the knowledge economy like finance. Previously, this kind of infrastructure required a significant human analyst labor force to curate, contextualize, and extract meaning from the data. That’s why Bloomberg Terminal made sense for finance — where an information gap translated directly into significant margin. But when the analyst layer is AI, this model becomes viable for industries where the margin upside is smaller, but the information advantage is still transformative. AI has unlocked the ability to build the “Palantir for X” across domains that were previously too complex or economically inefficient to support such an intelligence layer.
Why “Palantir for X” is inevitable — but not enough
The rise of “Palantir for X” startups is inevitable, largely due to shifts in the AI and data infrastructure landscape. In Palantir’s early days, building AI-powered decision intelligence required solving foundational technical challenges from scratch — data integration, machine learning, and analytics had to be developed in-house. Today, many of those barriers have disappeared.
The proliferation of pre-trained AI models and LLMs means startups no longer need to build AI from the ground up. Instead, they can fine-tune existing models that already understand industry-specific concepts, allowing them to focus on embedding intelligence into workflows rather than reinventing AI from scratch. Cloud-native data infrastructure from AWS, Azure, and Snowflake now handles large-scale data storage and processing, eliminating the need to build custom data pipelines. API-first architectures have made enterprise integration dramatically faster and easier, allowing AI platforms to plug into existing workflows with minimal friction.
These changes have lowered the cost and complexity of building AI-powered decision intelligence, making “Palantir for X” startups not only possible but inevitable. Unlike Palantir, which had to generalize across multiple industries, vertical AI startups can focus deeply on one sector, embedding themselves in industry-specific workflows and accelerating adoption. This is why we will see a surge of new AI companies attempting to dominate vertical markets — from supply chain logistics to clinical decision support to risk management in finance.
However, while this trend will produce a wave of new companies, it is not the ultimate prize. The real question is not who can replicate Palantir’s enterprise model in a new vertical, but who can own the decision-making layer of an entire industry in a way that Palantir never could.
The winning AI startups will own vertical intelligence layers
The most defensible AI companies of the future will go beyond aggregating data and offering predictive insights. They will embed themselves into the decision-making fabric of entire industries, becoming the intelligence layer that businesses and regulators alike rely on. This defensibility will be driven by three key factors.
First, the most successful vertical AI startups will capture and structure proprietary industry knowledge in a way that has never been done before. While existing AI solutions primarily analyze and synthesize available data, the next generation of AI companies will generate entirely new, industry-specific data sets and codify the kind of tribal knowledge that professionals rely on but that has never been formally documented. In the healthcare industry, for example, an AI-driven ontology that captures best practices for rare disease diagnosis — structured from millions of patient cases, physician notes, and treatment outcomes — would become an intelligence layer that every hospital, insurer, and regulator would depend on.
Second, once an industry adopts a shared ontology, network effects will reinforce its dominance. Every new participant — whether a supplier, customer, regulator, or service provider — adds more data and decision logic, strengthening the system’s intelligence and making it increasingly difficult to operate outside of it. This is similar to how Visa and Mastercard became dominant in financial transactions: once a critical mass of merchants, banks, and consumers used their networks, participation became unavoidable. An AI-powered industry ontology will follow the same trajectory, becoming the standard decision-making framework for an entire market.
Third, the most successful AI ontologies won’t just provide insights; they will be embedded in the critical decision-making workflows of an industry. The most valuable AI companies won’t just analyze how businesses operate; they will actively run those operations. Whether it’s optimizing risk models in finance, dynamically adjusting supply chain logistics, or automating clinical decision-making in healthcare, these AI platforms will become the central nervous system of their respective industries.
The real question: who will define the next trillion-dollar industry?
Palantir proved that AI-powered decision intelligence is an enterprise necessity, but its high-touch, service-heavy model won’t scale to every vertical. The companies that define the next era of AI won’t just replicate Palantir — they will rethink how entire industries make decisions at their core.
This is why companies that build industry-wide ontologies have the potential to be much larger than Palantir. Rather than selling software to enterprises one by one, they will own the underlying decision-making infrastructure that an entire market depends on. This shift unlocks exponential growth through network effects, deeper moats through standardization, and new revenue models that extend beyond enterprise SaaS contracts into transaction-based monetization.
In the same way that Bloomberg defined how financial professionals consume market data, or Visa became the infrastructure layer for payments, the most valuable AI companies of the future won’t just provide software — they will define how industries function.
The question for investors is no longer, “Which AI startup has the best model?” but rather, “Which company is building the ontology that will define the next trillion-dollar industry?”