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With AI, software has gone from packaged tools to embedded intelligence that performs work, rather than just supports it. Companies that still compete on features and charge for seats need a new plan - fast.
AI tools are empowering non-technical users to build solutions, but complex business rules keep SaaS essential. The future blends buying core systems with AI-driven, user-built customizations.
AI products differ from traditional software because they're non-deterministic and must balance agency with control. This post introduces a framework that helps teams ship responsibly by starting small, iterating with evals, and gradually granting AI more autonomy to build trust and stability.
Startup advice is most useful when it's specific, contextual, and constraint-aware, showing the messy decision-making process, trade-offs, and lessons learned rather than abstract best practices or universal principles.
Performance management is difficult but normal. Managers can handle it respectfully by focusing on role fit, giving clear feedback, offering chances to improve, and acting decisively when needed.
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Product-centric teams exhibit specific behaviors like context-sharing, delayed convergence, and direct customer engagement that foster better decisions, sharper focus, and meaningful progress. Cultivating these habits requires space, reflection, and intentional tradeoffs.
Generative AI lets non-engineers build internal tools with natural language, speeding up iteration and ownership. As capabilities mature, prototypes are evolving into secure, production-ready systems across organizations.
You can't change your manager, since structural dynamics give them the final say. The best path is to adapt, focus on where you have leverage, and learn from their perspective.
A CEO-specific GPT lets comms teams instantly generate authentic quotes, briefings, and messaging in the leader's voice.
Early-stage products thrive on concrete goals.