Code is Cheap. Decisions are Expensive

The Cost of "How" Has Collapsed

For the last forty years, the biggest limit in software was implementation. If you had a great idea, the hard part was actually building it. You needed to hire expensive engineers, manage complex schedules, and wait weeks or months to see if it worked. The "How" was the most expensive part of the equation.

That era is over. AI coding agents have collapsed the cost of implementation. Today, you can describe a feature, and an agent can write and debug the code in minutes. The barrier to entry for creating software has dropped from a wall to a speed bump. We have successfully automated the act of typing syntax.

The New Bottleneck is "What"

But this speed creates a new problem. When the factory floor speeds up by 100 times, piles of unfinished work start stacking up at the front door.

Now that shipping is instantaneous, the bottleneck has shifted upstream to decision-making. The "What" and the "Why" are now the most expensive parts of the process. While the AI can write the code instantly, the humans are still stuck in the same slow loops:

• Debating which feature matters most.
• Writing vague instructions that confuse the team.
• Sitting in meetings trying to get product, design, and engineering to agree.

The coding has accelerated, but the thinking has not. We are trying to feed a supersonic engine with a fuel line that drips at a human pace.

Building the Wrong Thing Faster

There is a danger in this new dynamic. When code is cheap, the temptation is to build everything. Because it costs so little to try an idea, we stop checking if the idea is actually good.

This leads to a paradox where we ship more features than ever but don't actually solve more problems. We are simply building the wrong things faster. We create "instant junk" where software that works technically but serves no business purpose. It creates clutter and confusion, not because the code is bad, but because the decision to build it was rushed.

The Value of Precision

In this new world, the high-value skill is no longer knowing how to write code. It is knowing how to define it. If an agent can build anything you ask for, the most valuable person in the room is the one who knows exactly what to ask for.

This shifts the focus to AI Architecture. The work is no longer about managing lines of code. It is about managing ambiguity. The new masters of the craft are the people who can:

• Create rigorously clear specifications.
• Define the exact logic of a system before the work begins.
• Get the team to agree on the "Why" so the AI can execute the "How" without making mistakes.

Conclusion

We used to value engineers for their ability to solve technical puzzles. Now, we must value them for their ability to make choices. The implementation is now a commodity. The real scarcity is found in the slow, messy, human work of figuring out what actually matters.