Another CIO commented that his organization had accepted not always using the latest AI models and tools because it could not sustain the pace of change. I can understand that choice. It also made me think about the opportunity for an AI-native startup designed from the start to adapt closer to that pace. If that ability improves how the business operates, the advantage could be powerful.

I'm exploring that possibility by looking at how to preserve startup knowledge while keeping tool and agent choices open. That includes representing decisions and project context in artifacts that different tools could access. My experience at OKA helps me think about making those changes manageable. There, I built an IT organization capable of continuously updating software dependencies, supported by architecture, automation, and technical excellence. I'm looking for a decomposable design for this exploration: separate parts with defined responsibilities for maintaining knowledge and making it available to tools.

I expect that separation could make tool changes easier, but defining and maintaining the boundaries takes work now. The knowledge's representation and delivery may also need to change; access to the same artifacts would not ensure consistent interpretation or behavior. I'm still working through how much adaptation and revalidation each change would require, and whether this approach could help the startup adapt faster in ways that create a business advantage.