Instead of spending too much time on building an AI core product, launch smaller experiments to gather early user feedback before full integration.

Experimentation is key in pricing strategy, especially for AI products. Testing different pricing models helps determine what resonates with customers and drives value.

Experimenting with different AI models is crucial, as their performance can vary significantly across tasks like debugging, planning, and implementing features.

In product development, it's vital to communicate the capabilities and limitations of AI models upfront, ensuring users have the right expectations about what the technology can and cannot do.

Live demos involving AI can be unpredictable, as they often yield different results each time.

Having a dedicated experimentation team allows for faster innovation and the ability to fail fast, which is essential in a rapidly evolving AI landscape.

Creating a culture of experimentation and innovation is essential for businesses to thrive in the fast-paced AI landscape.

In the AI era, things will change every day. If you wait too long, you may never be able to launch your product.

Many AI products start in a lab or beta phase, and only after stabilizing and reducing costs do they become core offerings.