Hey friends, happy Monday!

If you’ve been experimenting with AI, you’ve probably had this moment.

You paste something into ChatGPT or Claude.
The output is surprisingly good.

For a second you think:

“We could build a product around this.”

And you might be right.

But here’s the part that most teams discover too late:

The distance between a cool AI output and a reliable AI product is much larger than it looks.

Many AI projects stall because teams jump straight into building infrastructure before proving the idea works. They spin up vector databases, design complex pipelines, and build elaborate architectures.

Then three months later they realize something uncomfortable.

The core task itself is unreliable.

The real work in AI product development is not infrastructure.

It is prototyping, testing, and learning quickly.

In today’s edition we will explore:

• Why AI prototyping is different from traditional prototyping
• The fastest way to test AI product ideas
• How to structure early experiments
• The common traps that slow teams down
• And a practical workflow for turning experiments into systems

Let’s explore.

— Naseema Perveen

If you’re a decision maker at your company, you need to be on the bleeding edge of, well, everything. But before you go signing up for seminars, conferences, lunch ‘n learns, and all that jazz, just know there’s a far better (and simpler) way: Subscribing to The Deep View.

This daily newsletter condenses everything you need to know about the latest and greatest AI developments into a 5-minute read. Squeeze it into your morning coffee break and before you know it, you’ll be an expert too.

Subscribe right here. It’s totally free, wildly informative, and trusted by 600,000+ readers at Google, Meta, Microsoft, and beyond.