Monetisation: Building Products That Use Claude

Series: Claude Learning Journey · Expert

The honest truth about monetising AI products is that most of them should not exist. Not because the technology is bad — it is remarkable — but because the economics rarely work. Token costs, inference costs, and the cost of human oversight make it very hard to build a profitable product where AI is the core value proposition.

The products that work are the ones where AI is genuinely cheaper than the alternative, or where it creates value that customers will actually pay for rather than get for free from a chatbot. This post is about thinking clearly about which category your product falls into.

The Economics of AI Products

When you charge for an AI product, you are charging for the AI plus the product. The AI is the mechanism; the product is what the customer buys. If the AI is the product — if the customer is paying for the AI and not much else — your margins are under constant pressure from falling token prices and the fact that your competitors have access to the same models.

The products that sustain pricing have something else: proprietary data, a specific domain that requires expertise, a workflow that is genuinely painful to automate without deep integration, or a community that generates value from the AI layer rather than being charged for it.

Where AI Actually Adds Value in a Product

AI is worth building into a product when it makes something possible that was not possible before, or makes something dramatically cheaper than the alternative. Specifically:

Where the task is too complex for rules-based automation but too expensive for human execution at scale. This is the sweet spot.

Where the AI handles the ambiguity that would require significant human judgement. Where the cost of a wrong answer is manageable and the value of speed is real.

Where the AI is part of a workflow and the product manages the surrounding complexity — data ingestion, storage, presentation, integration — not just the AI itself.

What This Means for Your Product

If you are building an AI-first product, the questions to ask:

Can customers get the same AI capability for free elsewhere? If yes, your product needs to offer more than the AI.

What is the cost per request at your target volume, and what is the customer willing to pay? If the gap is negative, you do not have a business model.

How does your cost structure evolve as token prices fall? Products built on AI APIs will get cheaper as the underlying models get cheaper. Products built on custom infrastructure may not.

What You’ll Learn

• Why most AI products have bad economics
• The three things that make an AI product sustainable
• Where AI genuinely adds value versus where it is a feature
• The questions to ask before building

Try It Yourself

Take a product idea you have been considering. Work out the cost per user per month at a realistic usage level. Compare that to what you could charge and what the customer would pay. The gap is your business case. If the gap is negative, either the product needs a different AI approach or it is not a product.

What’s Next

Building a product means understanding the people who use it. The next post is about the community around your product — how to build it, grow it, and measure whether it is working.

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Part of the Claude Learning Journey series · Next: Community: Building an Audience Around Your Product