I’ve been thinking about how we can integrate AI into society in a way that mirrors the way we already structure things. We have individuals, families, communities, and then bigger entities like states and nations. What if AI worked in a similar way? This idea has been bouncing around in my head for a while, and today it really started to take shape.

At the individual level, you’d have your own personal AI, running on your own hardware, using your device’s compute power. This AI is like an extension of yourself—your sidekick, totally in sync with your values and personality. But its power is limited to what your personal hardware can handle and the data it gets from just you.

Then, at the family level, the AI operates on more devices. It has access to more compute power and a larger pool of data, because it’s pulling from everyone in the family. It’s designed to understand the collective goals and values of the group, but it’s also balancing input from each individual. It’s a little less aligned with you specifically, but still closely connected.

And this trend keeps scaling up. Community AI has even more compute power and data, pulling from multiple families and individuals. By the time you reach state or national AI, it’s working with vast amounts of data and compute power, solving problems for a much larger population. Each layer of AI has its own role, balancing alignment between personal values and the collective good, with higher levels handling broader, more intricate challenges.

At the societal level, AI would have the most resources—more data, more compute power—and work to address collective challenges. It’s not about replacing human governance or leadership, but rather augmenting it. AI at this level would assist in navigating complex societal issues by analyzing vast amounts of data and offering insights that align both individual and collective interests. Human leaders would still be central, but AI would provide a layer of intelligence and problem-solving that could scale far beyond what humans can manage on their own.

Now, let’s fold in another idea I’ve been thinking about: a data economy. In the way we work today, you’re typically hired to perform a task—let’s say writing code—and your value is based on getting that task done. You’ve got a manager supervising the work, assigning features or tasks, and you focus on delivering them. But what often gets overlooked in that structure is the meta layer—the process of improving your craft, finding better ways to automate, or pushing quality higher. That kind of personal growth is assumed to happen over time as you advance in your career, but it’s not usually the focus.

In the data economy, it shifts. Instead of being hired just to write code, the AI might handle most of that output, and your role would be to guide the AI—help it improve, get better at the task, and find more efficient solutions. Your value isn’t about doing the task directly anymore; it’s about your expertise, your creativity, and your ability to teach and refine the AI in a specific field. Your approach to problems becomes something that you can own, something that’s inherently valuable. This puts more emphasis on honing your craft and developing domain-specific knowledge, where you’re more focused on creativity and problem-solving than just checking off tasks.

The AI frees people up from the repetitive, tedious parts of jobs and pushes most careers toward the creative frontier. It’s about shifting the majority of jobs toward work that requires passion, expertise, and innovation. That said, I think there’s still room for menial tasks that some people actually prefer, but the focus moves toward elevating creativity and skill in the workforce.

Obviously, there are going to be concerns. Some people don’t want to constantly innovate or think critically—they prefer stability. And that’s a big one. There are people who just want to show up, do the job, and go home without worrying about whether they’re the next big innovator. I get that. Some people value routine, stability, and predictability over creativity and exploration. But in this new structure, those repetitive tasks are the very ones we’ll try to automate first. The goal isn’t to make people obsolete but to free up time for higher-level work that only humans, with our creativity and adaptability, can provide.

There’s this balancing act between the need for alignment and the hunger for unique data. Society wants us all to be on the same page, but AI, especially at the higher levels, wants as much diverse data as it can get. It needs those outlier ideas to keep improving. It’s kind of like how governments work now—we need order, but we also need innovation. The tension between these forces can actually be a good thing.

And on a related, slightly funny note, think about how personalized AI could even start affecting something like schoolwork. Imagine students using AI to help write their homework or essays. Teachers grading these AI-assisted papers might give feedback like, “This sounds way too artificial, like it was written by an AI,” and give it a bad grade. The student could then take that feedback, tell their AI it needs to sound less robotic, and then boom—next time, the AI’s output is more aligned with the student’s voice, more unique, and gets a higher grade. It’s kind of wild to think how AI will adapt to even small critiques like that, improving over time based on the person’s data and style. The AI will get better at mimicking the student’s voice, making the collaboration between the human and AI even stronger.

This is where I think the future is heading. We won’t value people just for their labor anymore. We’ll value them for the way they think, the data they provide, and how they help AI solve problems in new, creative ways. It’s a shift in how we think about work, value, and society.

Anyway, that’s what’s been bouncing around in my head today. I’m still fleshing it out, but I think there’s something to this idea of structuring AI in a way that mirrors how we already structure society. It’s about solving alignment issues between humans and AI, and finding a way to value human creativity and problem-solving at a deeper level.