The Side of AI I Want to Be On

I have been thinking a lot about what side of AI I want to be on.

I am a product leader, a composer, a person of faith, a husband, and a father. AI does not feel abstract to me because it touches nearly every part of the life I care about: work, creativity, learning, family, faith, and the future we are building for the people who come after us.

My answer is becoming clearer. I want to be on the side of AI that makes humanity better: the side that creates real business value while also helping people learn more deeply, create more confidently, access knowledge more easily, and spend more time solving meaningful human problems.

I am not interested in a version of responsible AI that treats business outcomes as suspect. AI products need to work. They should improve productivity, generate revenue, save time, increase adoption, transform workflows, and make operations more effective.

I have seen that potential directly through enterprise AI adoption, agentic workflows, productivity tools, and experiments designed to help real people do their work more effectively. The strongest programs begin with a measurable problem, fit the technology into a real workflow, and learn from what people actually use.

But efficiency is a means, not a complete vision. If the only story we can tell about AI is that it lets an organization do the same work with fewer people, we are leaving much of its value unexplored.

At Guitar Center, I helped run surveys about AI adoption. We asked about time savings because productivity was an obvious outcome to measure. We also asked whether AI improved the quality or creativity of people’s work.

I expected the productivity gains. What surprised and inspired me was that a majority of respondents reported increased creativity.

That changed the way I thought about the opportunity. AI was not only helping people complete the same task faster. It was helping them explore alternatives, get unstuck, shape rough ideas, and create with more confidence. The technology could reduce friction without reducing the person. In the right product and workflow, it could expand what someone believed they were capable of doing.

There is still a real tension here. AI can save money, and organizations have a responsibility to operate effectively. But saving money by quietly changing people’s responsibilities, obscuring the intent of a rollout, or allowing uncertainty to spread is not good leadership.

If AI will reduce certain kinds of work or materially change a role, people deserve honesty. They deserve enough context to understand what is changing, support as they adapt, and a credible path toward the work that comes next.

Trust is not a communications layer added after the product decision. It is part of the product and operating model. Transparent expectations, human review, practical guardrails, clear accountability, and thoughtful change management all shape whether adoption becomes durable or corrosive.

I recently listened to part of Elder Gerrit W. Gong’s presentation on AI, faith, dignity, and human flourishing. It gave me clearer language for something I had already been feeling.

My faith informs the belief that people have inherent worth and should not be treated as inputs to optimize away. That conviction is personal, but the product principle is broadly human: technology should respect dignity, preserve agency, and help more people participate in creating value.

This is not an argument against ambition or scale. It is an argument for a more complete definition of progress.

In my professional work, that means building enterprise adoption systems, agentic workflows, productivity tools, and responsible change practices that connect AI to measurable outcomes without separating the technology from the people expected to use it.

In my personal exploration, it also means asking how AI can widen access to knowledge, support creativity, deepen scripture study, and accelerate life-sciences discovery. These experiments are not all production products or proven business outcomes. They are places where I am learning what human-centered AI could make possible.

Across both, the pattern I care about is the same: give people better tools for thinking, learning, deciding, creating, and contributing.

For me, AI for human flourishing is not a soft alternative to product rigor. It creates harder and more useful questions.

Did the product create measurable value? Did it improve the quality of the work, not only its speed? Do people understand when and how AI is involved? Are the boundaries and review paths clear? Did the system expand access or capability? Did we help people adapt to the changes we introduced?

Those questions make the product stronger. They force strategy, experience, governance, measurement, and change management into the same conversation.

Even if AI eventually takes over a large amount of today’s work, the human story does not end. There will still be people to heal, communities to build, truth to seek, art to create, systems to improve, diseases to fight, children to teach, and human potential to unlock.

The opportunity is not simply to remove people from work. It is to help more people direct their time, judgment, creativity, and care toward work that matters.

That is the side of AI I want to be on. I want to build AI for measurable impact, responsible adoption, and human flourishing.