I have been experimenting with OpenClaw as another way to understand where AI-assisted software work is heading, and what happens when the cost of producing code keeps falling.

What interests me is not only whether a tool can produce code. The more useful question is how it changes the shape of the work: how ideas become requirements, how assumptions are tested, how quickly a prototype becomes something reviewable, and where human judgment is still doing the important work.

For a long time, a lot of software decisions have been constrained by the cost of getting something built. That constraint is not disappearing, but it is changing. Coding assistants and agentic tools are making it easier to move from an idea to a working first version, especially for contained internal tools, prototypes, and workflow applications.

My early impression is that these tools reward clarity. If the goal is vague, they can produce plausible noise. If the goal is specific, and the review loop is disciplined, they can make small experiments much easier to start.

That shifts the leadership question. If software becomes cheaper to generate, the scarce thing is not only development capacity. It is knowing what should be built, what should not be built, what needs proper governance, and what can safely remain an experiment.

That matters for software teams, but it also matters for leaders and boards. AI coding tools are not just a productivity story. They raise questions about assurance, maintainability, security, skill development, and how organisations know whether the software they are creating is actually fit for purpose.