I mostly agree with you, especially on software design being underappreciated. A lot of what slows teams down today isn’t typing code, it’s reasoning about systems that have accreted over time. I am thinking about implicit contracts, historical decisions, and constraints that live more in people’s heads than in the code itself.
Where I’d push back slightly is on framing this primarily as an LLM limitation. I don’t expect models to reason from first principles about entire systems, and I don’t think that’s what’s missing right now. The bigger gap I see is that we haven’t externalised design knowledge in a way that’s actionable.
We still rely on humans to reconstruct intent, boundaries, and "how work flows" every time they touch a part of the system. That reconstruction cost dominates, regardless of whether a human or an AI is writing the code.
I also don’t think small teams move faster because they’re shipping lower-quality or more experimental software (though that can be true). They move faster because the design surface is smaller and the work routing is clear. In large systems, the problem isn’t that AI can’t design; it’s that neither humans nor AI are given the right abstractions to work with.
Until we fix that, AI will mostly amplify what already exists: good flow in small systems, and friction in large ones.
I've been thinking about why AI seems to accelerate some teams dramatically while leaving others mostly unchanged. This post is an attempt to articulate what I think is missing: not better tools, but better routing of work, context, and ownership. Curious how this resonates (or doesn't) with others.
Where I’d push back slightly is on framing this primarily as an LLM limitation. I don’t expect models to reason from first principles about entire systems, and I don’t think that’s what’s missing right now. The bigger gap I see is that we haven’t externalised design knowledge in a way that’s actionable.
We still rely on humans to reconstruct intent, boundaries, and "how work flows" every time they touch a part of the system. That reconstruction cost dominates, regardless of whether a human or an AI is writing the code.
I also don’t think small teams move faster because they’re shipping lower-quality or more experimental software (though that can be true). They move faster because the design surface is smaller and the work routing is clear. In large systems, the problem isn’t that AI can’t design; it’s that neither humans nor AI are given the right abstractions to work with.
Until we fix that, AI will mostly amplify what already exists: good flow in small systems, and friction in large ones.