Published on
August 5, 2025
The early winners in SaaS built systems of record—structured, scalable backbones that stored critical business data. Think Salesforce, Oracle, Workday. These weren’t flashy products, but they were dependable—and they powered the first wave of digital transformation.
As infrastructure has become commoditized, the bar has moved. The next generation of software companies won’t win by simply organizing data. They’ll win by understanding it.
The future belongs to systems of context: applications that adapt in real time, interpret intent, and deliver intelligence exactly when it's needed.
This isn’t just a UX improvement. It’s a full-stack rethinking of how software is designed, how data is captured, and how intelligence is delivered. We’re in the midst of a shift from systems of record to systems of context.
Why Context Matters in AI
AI applications need context—who the user is, what task they’re trying to complete, and what’s happened so far. More importantly, the right context at the right time is critical. Relevant context significantly improves performance. For instance, Anthropic performs better with 30K tokens than with 200K tokens—if the context is right.
Relevance also means that context is always evolving. World-class products will win by being the best at capturing unstructured signals and human processes, and filtering out what matters. This means apps will dynamically adjust. The schema will adapt. The interface will evolve. The goal is no longer just to store data, but to continuously learn from it.
Take CRM. In the old model, users had to search for accounts and update their pipeline manually. In a context-rich system, the interface adapts—surfacing the most relevant account information and even suggesting next-best actions.
Prospecting? Surface ideal customer profiles, draft emails, and call history.
In late-stage negotiations? Show pricing details, past conversations, and relevant sales enablement materials.
Post-sale? Highlight implementation plans, support tickets, and contract terms.
It’s the same data, but the software shows different information at each moment—because it understands context.
Opportunity Exists in Every Space
This shift isn’t limited to CRM. Every category will be reshaped by software that captures and applies context in real time. In logistics, for example, traditional TMS platforms don’t account for which carriers prefer which loads or which routes are most efficient in real-world conditions. That knowledge is tribal—it lives in heads, emails, and muscle memory.
An AI-native TMS should infer that context from interactions, emails, and route data—then surface recommendations without the user needing to ask.
What We Look for in AI Startups
As early-stage investors, here’s how we evaluate companies building in this space:
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AI-native architecture: Founders starting from a clean slate have an edge. Retrofitting context-aware logic into legacy systems is hard.
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Strong context modeling: We care less about top-line metrics and more about how well the product captures and uses real-world signals. Token-level usage and contextual awareness are early indicators of stickiness.
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Full workflow coverage: Shaving a few minutes off a task isn’t enough. The strongest companies solve the entire business process end-to-end.
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Pricing that aligns with value: Context volume will vary by customer. Teams need thoughtful pricing models that reflect this without creating billing surprises.
Master Context, Define the Decade of Enterprise
In the 90s, software was about databases. In the 2000s, it was about cloud access and better UX. Today, it’s about building software that understands the work itself.
The future isn’t bots layered on top of legacy tools. It’s context-first platforms that turn AI into a true collaborator—platforms that know what matters, adapt to each user’s role and moment, and act with precision.
We're still in the early innings. But we believe the companies that master context will define the next decade of enterprise software.
And we're backing the founders who see it coming.