AI Didn’t Speed Up Development. Here’s What It Actually Did:
When generative AI burst onto the scene, everyone assumed developer speed would skyrocket. Code autocomplete became full-on agentic workflows. IDEs got smarter. Everyone shipped faster… right?
Not exactly.
Despite all the hype, new data shows that AI is actually slowing developers down. According to a recent study from METR, developers using AI tools like Cursor experienced a 19% slowdown in task time, the opposite of the anticipated 24% speed boost.
AI Makes You Feel Smarter, But Slows You Down
The root problem? Over-reliance. Once developers sense that the AI can do the job, they offload the thinking. They stop making critical decisions and instead keep tweaking prompts or waiting for the assistant to “get it right.”
It’s like the MIT study, where researchers found that students using ChatGPT stopped thinking analytically because they didn’t need to. The same brain-offloading behavior is now infecting dev workflows.
That lazy loop of “generate → edit → regenerate” eats time and introduces bugs, churn, and false confidence. It creates the illusion of speed, but results in longer delivery timelines, confused teammates, and frustrated managers.
So instead of helping developers sprint, GenAI tools often trap them in a debugging maze of their own making.
Faster Expectations, Slower Delivery = Executive Panic
This mismatch between AI promise and actual velocity is creating friction at the top. Executives expect GenAI to be a turbo button for engineering teams. Instead, they’re seeing rising costs, inconsistent output, and fractured workflows.
The discrepancy has made many leaders feel as though GenAI is “tearing their companies apart,” not because AI is inherently harmful, but because it’s misapplied and often under-managed.
🚀 The Real Impact: Changing Who Gets to Build
Here’s the part everyone’s missing: GenAI isn’t about accelerating the fastest developers. It’s empowering the rest of the team to contribute meaningfully.
Before GenAI:
- Only senior devs could take on complex UI tasks.
- Designers handed off mockups and hoped for the best.
- Junior engineers hit blockers and waited for reviews.
- Product managers had ideas, but no way to test them.
Now?
- Designers generate production-grade UI components from Figma and language prompts.
- Junior devs complete tasks they previously couldn’t even start.
- Non-devs build, test, and ship meaningful changes, often without writing a single line of code from scratch.
This is a reallocation of capability, not just a reallocation of speed.
Instead of helping 10x engineers ship 12x faster, GenAI is letting more people become builders. The long-term benefit isn’t about shipping faster; it’s about broadening who can ship at all.
Rethinking the Metrics
If you’re measuring success by individual dev velocity, you might conclude that GenAI is failing. But if you zoom out, you’ll see the organizational velocity increasing:
- More parallel workstreams
- Fewer dependency chains
- Reduced reliance on senior bottlenecks
- Higher resilience across roles
This is what it looks like when technology changes not just how we work, but who gets to do the work.
TL;DR
- GenAI tools like Cursor slow developers down by 19% on average
- Over-reliance on AI reduces critical thinking and increases iteration loops
- Execs are frustrated by the delivery gap between AI hype and reality
- But the real win? AI enables non-devs and juniors to contribute meaningfully
- This broadens contribution, reduces bottlenecks, and transforms team dynamics
GenAI didn’t speed up your best devs — it created new ones.
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