The framing that dominates most enterprise AI conversations is model-centric: which model is best, how does GPT-4 compare to Claude, which provider offers the best cost-per-token for a given task. This framing misidentifies the bottleneck. The model is rarely what constrains enterprise AI performance. What constrains enterprise AI performance is the data that reaches the model — its freshness, its accuracy, its format, its completeness, and the latency at which it arrives.
Enterprise data is structurally hostile to AI use. It's split across ten systems. It's normalized according to schemas that were designed before anyone thought about AI access patterns. It's updated at batch intervals that make it hours or days stale by the time it reaches an AI pipeline. It contains sensitive information that creates compliance risk if it's exposed to an external model provider without transformation and filtering. These are plumbing problems, and they don't go away by choosing a better model.
The freshness problem
Real-time enterprise AI applications — customer service AI, financial monitoring AI, inventory management AI — require access to data that reflects the current state of the business, not the state of the business as of last night's batch job. This is obvious in principle and consistently underestimated in practice. Teams build a prototype on a static dataset, demonstrate it working, and then discover in production that the data they need is a day old and the user experience is correspondingly broken.
The correct solution is to treat the data pipeline as a first-class deliverable of the AI project, not an afterthought. This means defining freshness requirements before building the AI layer, not after. It means investing in change-data-capture or event-streaming infrastructure before the model integration, not as a retrofit. It means understanding which data sources need to be real-time, which can tolerate an hourly refresh, and which are only needed for context and can be batch-loaded. These are infrastructure decisions, and they should be made with infrastructure engineering rigor — not the same rigor as the model selection, but the same rigor as the database design.
What this means for infrastructure investment
The investment thesis that follows from this analysis is straightforward: the companies that build the data access infrastructure for AI workloads will be as important to the AI application stack as the model providers themselves. Change-data-capture. Streaming normalization. AI-native retrieval stores. Document ingestion and parsing pipelines. These are the categories that receive less attention than model APIs in the current cycle — and accordingly, the categories where we believe the valuation-to-value ratio is most favorable for early-stage investment. Sequin's position in the CDC layer is a direct expression of this thesis.