Here are some real examples from our projects in 2025 at SIROC (for context: we are a 18 people venture studio; 140+ projects completed):

* A task estimated at 4 hours → solved with one well specified prompt

* A 20 hour engineering effort → executed in about 3 hours

* A 3 month project → delivered in 1 month

These are clearly best case scenarios. They are not the norm, yet. But they demonstrate what is possible.

We have also seen what happens when things go wrong. Companies, including startups, come to us with broken systems and spaghetti code and architecture caused by weak prompts, unclear requirements, and no verification.

It is important to understand that the efficiency gains we are seeing do not come from the tools alone. They come from a specific combination:

1) Engineers who have spent 20 years building everything from robotics to enterprise-scale technology. You cannot give a perfect instruction to an AI if you do not know what perfect looks like in a production environment.

2) A technical prompt should not be treated as a quick input or question. It is a detailed specification that requires experience and deliberate thinking.

3) Knowing the right combination of tools, workflows, and validation processes.

That said, some (many?) members of our team are dinosaurs in the software engineering world. They bring a ton of experience but are used to tools from 15 years ago and don't like change. We really had to push AI adoption (mostly Cursor and Claude Code) on them. It’s still an ongoing process, and probably will be for a while.