AI is not a product. It's an ingredient.
Everyone's pitching 'AI-powered' everything. But the best AI products don't lead with AI — they lead with the problem they solve. Here's why that distinction matters.
There’s a pattern I keep seeing. Someone builds a thing. The thing uses an LLM somewhere inside it. And then the pitch becomes: “It’s AI-powered.”
That framing is a mistake — not because AI isn’t real, but because it leads builders to optimize for the wrong thing.
The ingredient vs. the dish
Salt is in most good food. You don’t walk into a restaurant and order “salt-infused cuisine.” You order the pasta. The salt makes it work — but you don’t think about the salt.
That’s how the best AI products work. The AI is in there doing real work. But the user doesn’t think about AI. They think about getting their thing done faster, better, or without friction they used to have to deal with.
When your product pitch leads with “AI-powered,” you’re leading with the salt.
What good looks like
The best AI products I’ve seen — and the ones I think are worth building — answer a simpler question: what does this let someone do that they couldn’t do before, or do faster than before?
Not “what model does it use.” Not “what context window.” Those are implementation details.
The question is: what’s the user’s job, and how well does this help them do it?
So what does this mean for builders?
If you’re building something with AI in it:
- Lead with the problem. Not with the technology.
- Design the no-AI fallback. If your product breaks when the model hallucinates, you’ve made the AI the product instead of a component.
- Measure outcomes, not impressiveness. “The output sounded smart” is not a success metric.
AI is a genuinely powerful ingredient. But a great product is still a great product first.