The StoryBehind ACE

In early 2023, as Generative AI began moving into everyday life, the three of us found ourselves wondering what its rapid development would mean for people like us.

We had spent much of our careers building things from scratch. Two of us came from entrepreneurship and venture building. The third came from design, branding, and communication. We had always valued our ability to think, solve problems, create ideas, and turn those ideas into something real.

But as AI became more capable, we began to question some of our most basic assumptions. What if many of the abilities we had worked so hard to develop were about to become standard equipment? If knowing more, analyzing faster, producing better work, and executing efficiently became available to almost everyone, would those abilities still set us apart?

And if not, what would?

We did not have an answer, so we decided to explore the question ourselves.

Over the next three years, we conducted a social experiment across Taiwan and Japan. More than 500 people between the ages of 12 and 65 took part. They included students, professionals, entrepreneurs, executives, respected CEOs, and retired CEOs.

We invited them into a series of creative challenges where they used AI to research, explore ideas, make decisions, design concepts, and build something that other people could see and respond to.

Then we observed closely.

We were not simply interested in whether AI could help people work faster. That part was already becoming obvious. We wanted to understand something more fundamental:

As we ran more workshops and observed people from different ages, professions, and levels of experience, a consistent pattern began to emerge.

For generations, education and work have trained us to know more, analyze faster, solve problems accurately, and execute efficiently. These abilities helped many of us build successful careers and distinguish ourselves professionally.

They still matter. But they are also the areas where AI is advancing most quickly.

Knowledge is becoming easier to access. Analysis that once required years of experience can now happen in minutes. Ideas, presentations, strategies, designs, prototypes, and business plans can be produced faster than most of us would have imagined only a few years ago.

These abilities are not disappearing, but they are becoming less rare. As they become widely available, it becomes harder to build meaningful differentiation around them.

That realization led us to a different set of questions.

If AI can help almost anyone produce an answer, who decides which questions are worth asking? Who notices the opportunities that others have missed? Who decides which problems are meaningful enough to solve? Who understands what people genuinely need? And who can turn an unclear possibility into something others can see, experience, discuss, and believe in?

This was where we began to see human value appearing.

The participants who created the most interesting work were not always the ones with the most experience, knowledge, or technical ability.

They were often the ones who stayed curious for longer. They looked beyond the first obvious answer. They connected ideas from different industries, technologies, business models, cultures, and personal experiences. They tried to understand real people instead of relying only on their own assumptions. Then they gave their ideas a form that others could understand, question, and improve.

What they were demonstrating was cross-disciplinary creativity: the ability to look beyond one's own domain, connect seemingly unrelated ideas and perspectives, and turn those connections into something meaningful and useful.

We came to see these behaviors as a distinct human capacity: a way of thinking and working that puts cross-disciplinary creativity into practice. We call it Applied Creativity.

Applied Creativity is not simply about producing more ideas, and it is not limited to people in creative professions. It is the ability to use curiosity, imagination, empathy, and judgment to decide what is worth exploring, which problems are worth solving, what should be created, and who it should create value for.

It can be distilled into a simple yet powerful self-mastery mental model:

The idea behind this mental model is practical. We do not need to compete with AI by trying to become better reasoning machines. Humans provide curiosity, choice, judgment, meaning, and direction. AI amplifies our ability to research, reason, design, communicate, and execute.

Having this mental model in place, we started developing methods that it could actually be practiced.