Weekend Reflections #1 | The Momentum Wave
Most ideas don't die because they are bad; they die when momentum breaks before testing. AI shrinks the gap between thought and test, creating a Momentum Wave where natural language lets anyone prototype. But beware: speed without customer validation is just fast failure.
[Views are my own]
Most ideas in knowledge work do not die because they are bad. They die because momentum breaks before they become testable. AI may change that.
Over the past few weekends, I have been experimenting with AI coding tools and felt something I had not felt in a long time: that intense flow state where thinking, making, and learning happen almost continuously. As someone leading product excellence, I want to understand these tools through direct use, not just theory. That experience made me think more clearly about what AI is actually doing when it works well.
In knowledge work, plenty of ideas fail because they are bad. But some fail for a different reason: they lose momentum before they become testable. One of AI’s most useful near-term effects is that it shrinks the gap between thought and test, preserving momentum when it matters most.
Before AI changes organizations at scale, it may first change something smaller and more immediate: how long an idea can stay alive before friction kills it.
The hidden friction in knowledge work
One of the biggest frictions in knowledge work is interruption. Some interruptions are external: meetings, emails, and context switching. They are constant, but often manageable.
The more dangerous interruptions are internal. They happen when thinking runs into a skill gap, a dependency, or a handoff before an idea becomes tangible.
You have a promising idea for a better way to solve a customer problem or remove an operational friction. But to test whether that approach is even viable, you may need to code a prototype, model the data, design an interface, or configure an API. Without those skills, exploration requires pulling in others. That means resources, prioritization, and delay before you know whether the core idea works.
The thread breaks. Momentum fades. Innovation dies. Quietly. Before the first test.
A lot of the AI discussion still focuses on role substitution: which jobs will change, which tasks will disappear, who replaces whom. Those are fair questions, but they can overshadow a more immediate shift.
The key shift is that natural language is becoming the interface. You no longer need to master Python, SQL, or design tools to explore feasibility. You describe what you are trying to learn, and the tool helps you get to a first judgment. This is not just faster prototyping. It expands who can prototype at all.
AI can sometimes keep the cognitive thread alive long enough to get to a first test. Not because it removes the need for expertise or produces production-ready work, but because it can generate enough structure, output, or direction to keep the work moving while the original problem is still alive in your head.
The Momentum Wave
I call this the Momentum Wave: a temporary phase in which the distance between thought and test becomes unusually short. What makes this different from simply "faster prototyping" is the preservation of cognitive continuity: you stay in the same problem context from question to first answer, without losing the original insight that sparked the exploration.