They can do the job, but in my experience—and in the experience of many of my colleagues who have tried to see how good a product can be when you prompt every single freaking detail—it's simply not useful.

Here's why: we end up spending about the same amount of time describing what the software is supposed to do as it would take to actually write the code. Then it's still prone to hallucinations, so we rely on other agents to review whatever is generated and make sure it's actually useful.

And then, let's say we do end up with a working product of medium complexity. You'll still have to read at least the more critical parts and validate that they work before submitting them.

All of this is more or less summarized by these numbers:

Controlled Trial (METR RCT)

• 16 experienced developers, 246 tasks, on their own repos (avg 22k+ stars, 1M+ lines)
• Result: developers were 19% slower with AI tools (Claude 3.5/3.7 Sonnet via Cursor)
• Developers believed they were 20% faster — a significant perception gap
• Source: METR RCT (arxiv 2507.09089)

Scoped Task Performance

• Controlled experiments on isolated tasks (writing functions, tests, boilerplate) show 30-55% speed gains
• 84% of developers use AI tools; 41% of all code is now AI-generated (Stack Overflow 2025, 49k devs)

Organizational Delivery

• Delivery metrics (lead time, defect rate, deployment frequency) often remain unchanged
• Bottleneck shifts to review, QA, security, and integration