AI sped up coding. Where did the bottleneck move?

AI agents are getting very good at coding, debugging, test generation, documentation, and a lot of engineering activities. That is real in many organizations. But if engineering was not the constraint to begin with, or now stops being the constraint because of these improvements, faster engineering output may just create more work waiting somewhere else.

So AI is great at improving engineering output. But when you look at the end-to-end lifecycle of development and product, this impact does not repeat in the same way, at least not right now, in other areas of the lifecycle.

It is harder to get AI to help with product discovery or product adoption. The observability feedback loop is harder. So many organizations let people improve in the areas where improvement is easiest. That creates local productivity, which is simply not enough.

You might already be seeing the congestion. Or you might be seeing a situation where the congestion is hidden, developing, or avoided temporarily because the engineering organization can use its improvements to catch up on things it has been struggling with for years. In any case, at a certain point, if your engineering organization is growing fast enough, it will stop being the constraint. It will stop being the bottleneck. The congestion will move elsewhere.

One useful thing to do is start visualizing this. Pay attention to where inventory is accumulating and where starvation is starting to appear. The classic way to look at this is to create the value stream flow for the entire lifecycle of features, products, and ideas that you are developing in your organization.

You may notice that things that were explored and built are starting to accumulate before shipping to production. Or maybe they are already shipped, you have continuous deployment, and the DORA metrics look good. But when you ask whether this work is actually adopted, used, and creating value for people, either it is not really used or you do not really know.

Once you recognize that your AI activity is turning into AI output, which is great progress, the next question is how to shift toward AI impact. Awareness is the first step, but then you need to use this view of end-to-end flow to focus on the bottleneck.

After you find the bottleneck, you can apply principles like the theory of constraints. Subordinate the focus and capabilities of both humans and AI to the current bottleneck. If the bottleneck is adoption of features you are building, or training users on those features, then your AI effort should not focus on accelerating coding. It should focus on how to use AI to drive adoption.

If engineers are not the bottleneck, maybe they are not the first people we should focus on with the deepest enablement effort. Maybe the bottleneck is product professionals. Maybe it is operations. Maybe it is users. We need to look.

Do not just help everybody scale. Focus on helping the constraint scale. You will get to everybody at some point. But when you look at enablement, guidance, support, and focus, the current bottleneck should be one of the criteria.

The more you think about your products, your customer factory, how you're creating value in your organization, where the constraints are, where the bottlenecks are, what the strategic focus is, the better results AI will give you.

The better results the people developing AI capabilities for the organization will be able to deliver.