The Secret Playbook of AI Products
Building successful AI products requires orchestrating four distinct but interconnected domains: Product Management, Data Science, User Experience, and Development. Each domain brings its own challenges and opportunities, but the magic happens at their intersections.
When you look at successful AI products, you'll notice they balance four distinct perspectives:
The learning system is the product.
Most people building AI products are playing the wrong game. They treat AI like a feature when it's actually a new operating system for product development. This mistake is so common that it has become nearly invisible – like water to fish.
Products aren't about features; they're about feedback loops.
Instagram could launch with filters because filters are deterministic – input A always produces output B. But ChatGPT couldn't launch with perfect responses because language is probabilistic. Instead, it launched with a learning system that gets better through use.
Consider how Claude evolved. The data scientists didn't just build models - they built improvement systems. The UX team didn't just design interfaces - they designed feedback mechanisms.
The Hidden Game
The best AI products start with a minimum viable intelligence (MVI) – just smart enough to create useful feedback loops. Everything else can be learned.
Data Strategy is Product Strategy most product managers think about data as something you collect after launch. With AI products, your data strategy needs to be your product strategy. The key questions aren't just "What features do users want?" but "What data do we need to learn those features?"
Measure Learning Velocity Traditional metrics like user growth and engagement still matter, but they're secondary to learning velocity. Track:
You can still generate high value with a model that has an 80% accuracy
The Compound Effect
The magic of this approach is that it compounds. Every interaction makes the system slightly better, which attracts more users, which creates more interactions. This is why AI products tend toward winner-take-all markets.
A product that improves 1% per week will be twice as good in a year. One that improves 2% per week will be four times better.
But this only works if you build the right learning systems from the start. You can't bolt them on later any more than you can bolt on network effects to a social product.
The companies that understand this are playing a different game. They're not just building products; they're building learning machines. Their initial releases might seem basic, but they improve faster than their competitors can copy them.
This is the secret playbook of AI products: start with learning loops, make feedback core to the product, and optimize for learning velocity over initial performance. Everything else is just details.