More than half of all venture capital invested last year flowed into companies labeled as AI. For many investors, that number triggers an immediate instinct: concentration risk. A single sector absorbing the majority of new capital looks, on its surface, like a classic setup for distortion. But the number is misleading. What appears to be dominance of one category is really the diffusion of a foundational technology into every category.
AI is not functioning as a vertical. It is behaving like a horizontal layer—an infrastructure shift that sits across industries rather than inside any one of them. Classifying AI as a discrete sector is a carryover from earlier funding frameworks, not a reflection of how value is actually being created. The moment models, agents, and automation systems become embedded in workflows from healthcare to logistics to finance, counting "AI companies" becomes as imprecise as counting "electricity companies."
The internet offers the cleanest parallel. In the early 2000s, investors tracked internet startups as a distinct class. The taxonomy collapsed once every company incorporated online distribution, data, and software. AI is following the same trajectory—only at a much faster pace. Today’s capital concentration is not a bet on a single thesis. It is the aggregate investment required to rebuild the technical substrate of modern business.
The real risk is elsewhere. Early-stage valuation discipline is showing strain, with pre-seed teams raising at caps north of $100 million before demonstrating usage, let alone market fit. This is not systemic overheating but localized froth—an environment where the strongest founders can raise too easily and weaker ones can raise at all. Investors should distinguish between inflated entry points and genuine overconcentration of capital.
The practical takeaway is simple: the question is not whether your AI exposure is too high. It is whether your AI exposure is allocated intelligently. Investors should separate infrastructure-level plays—models, tooling layers, workflow automation—from story-driven bets that depend on unproven distribution claims or speculative model advantages. The horizontal nature of AI means it will continue absorbing a disproportionate share of venture dollars. That is not a signal to pull back. It is a prompt to refine selection criteria, focus on defensibility, and underwrite the companies building the durable layers of the next operating system for business.
