With the beginning of the new AI wave, many large companies quickly started adding AI features to their products. In most cases, these features were simply “tacked on” and didn’t gain popularity.
But gradually, AI Native products began to emerge — those that reimagine specific use cases from the ground up and provide a fundamentally new experience.
Today, I want to break down how products like this manage to create real value and gain traction. And Granola AI is a strong example.
What problem Granola AI solves
Granola AI makes it easier to take notes during meetings and calls. It lets users stay focused on the conversation while automatically capturing all the key details in a clear, easy-to-access format.
This is especially useful for managers, entrepreneurs, and investors who spend most of their day in back-to-back meetings. Getting accurate, structured notes right after a call — and being able to share them instantly — saves a ton of time and energy.
How is additional value created
By the time Granola AI appeared, there were already many AI services for taking meeting notes. This is one of the most obvious use cases for speech recognition and LLM technologies.
But Granola AI’s creators made one very non-obvious decision that allowed them to achieve significantly higher quality notes and create additional value compared to competitors.
Instead of making notes completely automatically, they used a hybrid model: users make very short, one-word notes during the meeting, and AI supplements them, ensuring maximum relevance.
For example, it’s enough for a user to write down just a number, and Granola AI will extract from the conversation and add context about what that number means.
In contrast, most summarizers lack awareness of the user’s context and priorities, often omitting important details in their summaries. Granola’s hybrid model solves this problem, enabling it to deliver a “wow” effect within its target segment.
What else helped Granola AI transform the user experience?
1️⃣ A native Mac application that detects the start of calls and listens to the computer’s audio stream.
Granola AI automatically understands when a meeting has started and begins working. The service doesn’t need to be manually connected to the meeting and is compatible with any calling solution.
2️⃣ The ability to share notes and ask questions about the meeting.
Users can send links to meeting notes, and other participants can ask questions, clarify details, and review the topics discussed.
3️⃣ Search and analytics.
Users can quickly find information from meetings thanks to AI-powered search.
Growth model
Granola AI uses a bottom-up growth model that includes:
— A viral growth cycle
The product is built around a use case with built-in virality. After meetings, people often share notes with key moments and next steps. This creates a native mechanism through which other people learn about the service, start using it, and consequently help spread it further.
— Sales to companies with high Granola penetration
When a certain number of employees are using Granola AI, companies want to buy the enterprise version with necessary certifications, security features, and other capabilities for large companies. This mechanism allows Granola to enter medium and large companies without lengthy deals and the need to hire a huge sales team.
— Short-term spikes of new users
The viral and bottom-up sales growth cycles are fueled by one-time activities that bring relevant users to the product and accelerate the flywheel.
These activities include: launching on ProductHunt, complimentary posts on social media from popular people in the tech community (Nat Friedman, Des Traynor, Steven Tay, Ben Tossel, and others), reviews from bloggers, etc.
Conclusion
New technologies determine the specifics of how value is created (how the task is solved more efficiently). At the same time, the basic laws of creating value and delivering it to target users remain unchanged.
Granola AI is a good example of how AI Native products can not just add AI features, but rethink the user experience from scratch, finding more effective solutions for existing problems.
