Why does a personal finance dashboard matter, even when it’s something the world has never seen? Complex, queryable data with nearly instant analysis and advice, gorgeously rendered with nearly infinite options, is the future of… something. But people have better things to do than think about the questions they might ask a new tool.

The graphic: OpenAI and the National Bureau of Economic Research published a working paper in September 2025 documenting in extreme detail the topics of ChatGPT conversations. Scroll down to “From the Notebook / Resources.”

An AI can create a remarkable experience, but conversation is hard—it’s not passive.

The Plaid x Perplexity and ChatGPT products released this spring look awesome. But prompting is a complicated way to look at finances because it demands active thinking. The learning curve to get sharp results is steep, and fine-tuning responses adds work. The response to “make this easier for me” should not be a more complicated product.

The user’s path of least resistance is to ask short, easy questions, hope for decent answers, and move on. If the answers aren't clear, or if they are formatted in a way that’s hard to digest, the product fails.

That is an experience problem fueled by four years of hype.

AI works best based on specific, scoped goals and clever prompts. Without them, an AI guesses what the user wants, burns time and tokens, and vomits more words than substance. What is a human user going to do with 500 words of AI bloviating? Publish a Substack?

Banking is a necessary chore; only power users or consumers with a deep-seated need make it more than that.

Most people want quick, digestible answers to concrete questions. Using an AI to get precise, meaningful results for complex issues is exhausting. “How do I optimize my mortgage payoff?” is not a frequent question, and it requires follow-ups.

AI has a ceiling as a consumer product because it underperforms by overperforming for many users.

Conversational finance is the next wave because it has to be. The technology exists, so it will be built. But a tool does little without an operator, and it’s tempting to over-teach the tool while under-teaching the mental model.

Even after I dropped out of computer science, one concept stuck with me:

Big problems need to be small problems first.

It stuck with me because it matters to code, philosophy, writing, and annual resolutions. A big goal alone will be a mess, turn into a different goal, or be abandoned and conveniently forgotten. A magic bullet exists only in retrospect, because the problem was already solved; reverse-engineering is always easier than creation. And it’s work.

Not wanting to work is a human problem. That makes it a design problem. Things should just work. Consumers shouldn’t need to understand the underlying mental model. Any product that forces critical thought creates a problem to solve one.

The AI power user creates specific, useful results based on specific, iterative prompts. The average consumer won’t spend the time trying.

Thanks for reading Fintech Notebook: A weekly GTM intelligence briefing read by fintech operators, strategists, and investors. Connect on LinkedIn or visit my website @ tylerbrown.co.

AI disclosure: This article used AI to assist chart design and production and design and code the app proof of concept.