Theory Ventures charts AI market shift from models to inference in three-year retrospective

Three years ago, Theory Ventures launched with the thesis that AI would reshape software building, selling, deployment, and operations. The market has moved faster than even bullish expectations: frontier models shifted from delicate prototypes to production systems, open-source became a substitute for enterprise workloads, and inference—the process where an AI generates an answer—emerged as the primary value pool rather than model development itself. This acceleration has compressed the timeline for company maturity; what once took years for traditional software companies now happens in quarters for AI-native firms.

This time compression has redrawn the venture landscape. Seed rounds, once a standardized category describing companies with a product concept and early product-market fit, now span $1m to $500m depending on ambition and market promise. The best companies mature much earlier than prior software generations, and the venture ecosystem has learned to build AI-native software faster. Concurrently, the inference market is specializing—similar to how databases fragmented into OLTP, OLAP, vector, and streaming systems a decade ago—creating distinct infrastructure categories for video, batch, local, agentic, and real-time workloads. Enterprises are increasingly adopting local and open-source models over frontier closed-source alternatives for cost, latency, control, and data governance, a shift that allows frontier capabilities to compress toward consumer hardware within quarters.

Beyond infrastructure, new business models are emerging. AI advertising is surfacing as a subsidy for inference costs, allowing applications to grow usage and revenue together rather than in tension—native ad formats in AI conversations are producing click-through rates 4–5× higher than display advertising. Security and operations have become frontier categories: the attack surface is expanding as agents, plugins, and skills introduce new entry points faster than enterprises can review them, while agentic systems are beginning to reshape ERP and back-office operations that have resisted change for decades. Theory itself has become a proof point of the thesis—a small technical team (3 investors, 9-person intelligence organization) analyzing twice the investment opportunities of a conventionally scaled firm, demonstrating that AI-native organizations can operate at 10× the leverage of prior software generations.