I rebuilt our entire Experimentation platform in 71 hours using AI agents. New codebase, new architecture, fully functional.
And it taught me something uncomfortable.
The hard part is no longer the building. It is still knowing whether any of it matters to the people using it.
This week I was in a room with a CIO who wants her leadership team to rethink every SaaS system they run. Not upgrade them. Question whether they need to exist at all.
The reasoning: if AI can compress “making and doing” to near zero, the bottleneck isn’t execution anymore. It’s choosing what to execute.
She’s right.
I work with product teams who can architect and ship sophisticated AI products in days. The ones who succeed aren’t the fastest builders. They’re the ones who test demand before they build.
When building costs $2M and 6 months, you could justify skipping validation. The stakes forced natural caution.
When building costs $2K and a weekend, you lose that friction. And without it, teams ship ten things nobody asked for instead of one thing nobody asked for.
AI didn’t solve the innovation problem. It concentrated it.
The question was never “can we build it?” In 2026, the answer is almost always yes.
The question is still: should we build it?
If you can’t answer that with data, building faster just means failing faster.
Data > Opinion. Especially now.