Here is a strange fact about modern life: we have access to more information than any humans who have ever lived, and we remain remarkably capable of having no idea what to do.

Money is particularly good at exposing this problem.

You can know the S&P 500’s return to the second, calculate your retirement needs in six browser tabs, ask an AI to explain a backdoor Roth in the voice of Werner Herzog and still have a fairly basic question: Am I doing this right?

Increasingly, I think that question — and questions like it — will define the next era of financial technology.

Consider where Americans are right now. In August, ⁠The Conference Board found that people felt better about current business conditions and the job market, but worse about the next six months. That apparent contradiction makes perfect sense to me. A person can have a good job, a healthy 401(k) and a profound suspicion that something expensive is lurking around the corner.

Then there is the problem of averages.

At Datalign, where I lead marketing and communications, we have the unusual privilege of seeing what people actually ask for when they decide they want financial help. The differences are instructive. Someone may have spent decades accumulating $1.4 million and suddenly need to learn how to spend it. Someone else has $380,000, another decade or two of work ahead, and the nagging suspicion that a collection of accounts does not constitute a plan. Another already has an advisor and has decided, after 12 years, that “fine” is no longer worth the fee. These profiles come directly from patterns in thousands of people who have used our platform. 

They remind me why averages are useful for describing populations and fairly lousy at describing people.

Datalign started by using data and machine learning to understand two complicated things at once: the circumstances of a person looking for financial advice and the capabilities of the advisors who might serve them. That produced our first business, advisor matching. Years of doing it also produced something more interesting: data, technology and institutional knowledge about how people seek financial help.

We can now apply that knowledge to more than making an introduction. With Relationship AI, we can help an advisor understand a prospective client before they speak. With GEOs AI, we can help wealth managers figure out where they should grow. And with Halo, we’ve created a tool that helps a person explore a financial question in the context of his or her own circumstances.

There is a philosophy underneath all of that, and it is also why I can do my job here with a straight face.

I have spent my career in communications. I believe stubbornly in human writing, specific language and the radical proposition that you should mean what you say. AI has only strengthened that belief. When anybody can generate 2,000 competent words before breakfast, producing words becomes less interesting. Knowing something worth saying becomes more interesting. Knowing whom you are saying it to becomes more interesting still.

The same applies to financial technology.

Personalization without understanding is Mad Libs. Authenticity without substance is branding. Technology without either can simply help us produce the wrong answer faster.

We use the phrase “perfectly aligned” at Datalign. I like it because it describes an aspiration rather than a feature. The person, the question, the advisor, the technology and, yes, the words should agree with one another.

Perfect alignment is aspirational. I studied creative writing, so I am professionally comfortable pursuing things that are aspirational. But I believe every American should be able to find financial advice they can trust, from someone equipped to understand their particular life. I believe technology can get us closer to that.

For me, believing those things has to come before believing in the job.