AI is changing work. There’s opportunity in it, too.

Two recent Moonshots episodes brought this into focus. In episode #304, around 44 minutes, the discussion turns to the skilled people needed to build computing infrastructure. By 47 minutes, the panel is pushing back on the idea that people should simply give up as AI advances.

In episode #305, they explore the construction, local knowledge and coordination behind modular data centers. Later, the conversation returns to finding worthwhile problems to solve.

My takeaway is that opportunity exists both in building the infrastructure and in making the technology useful. The panel’s forecasts are still forecasts. But those two directions give us something concrete to investigate now.

One person might help keep a data center running. Another might use AI to build a tool for a local business. Someone else might help a nonprofit connect its people and systems. Each starts with a human need and a person willing to learn.

Behind an AI service are buildings, power, cooling, networks and equipment. That creates areas to explore for people who enjoy tangible work: electrical and mechanical trades, hardware operations, cabling, logistics, procurement and project coordination.

For someone tired of spending an entire IT career at a desk, that is an interesting possibility. Troubleshooting a network, managing an incident or coordinating vendors may give you relevant experience to build on. The question is which role uses those strengths—and what additional training it requires.

These paths are different. The Bureau of Labor Statistics describes a typical four- or five-year electrician apprenticeship. Hardware support has different entry requirements. A short course does not make the two interchangeable.

Try this: Find three current openings near you across hardware operations, facilities and project coordination. Compare the actual work, schedule and entry requirements. Then ask one practitioner what a normal week feels like. You may discover a direction you had never considered.

If you enjoy software—or have an idea you have never known how to build—AI offers another direction to explore.

An AI coding tool can help you make a prototype, explain unfamiliar code and test possible approaches. The valuable skill is learning to guide that work: describe a real problem, judge the result, find what is wrong and improve it. Experienced developers bring important knowledge to that process. New builders can start learning it through a small project they understand.

I see this in Nspired’s work. Our Jira-connected governance portal brings operational information into a form people can use. Our MenLiving work connects a website, a community and the processes around participation. The opportunity begins with understanding the people using the system.

Try this: Choose one recurring frustration you know well. Write down who experiences it, what a better outcome would be and how you would test it. Build one small part with fictional data, then ask someone to try it. Keep the project small enough to understand and check.

Not sure where to begin? Get started with AI. Start with your life. walks you through choosing an assistant, giving it useful context and trying one real task—with a free worksheet to help you begin.

Many useful opportunities sit between the technology and everyday work. A business owner needs customer inquiries handled reliably. A nonprofit needs volunteers to find the right information. A team needs its tools to share information without someone copying it by hand.

Someone who understands those needs can help with process design, tool setup, training, integration and ongoing support. That could become a responsibility within your current job, a useful community contribution or the start of a service business.

Knowing the people and earning their trust matters. AI can help with the work; you still need to listen, make sensible choices and take responsibility for the result.

Try this: Ask a small-business owner or nonprofit leader to show you a task they repeat every week. Look for one modest improvement you could test together. Measure whether it actually saves time or makes the experience better.

You do not need to choose a new career today. Set aside an hour this week to explore one of these directions. Read a real job description. Have a conversation. Sketch an idea. Try a small learning project.

The career worksheet helps you compare two possibilities and plan a 30-day experiment around your time, responsibilities and budget. Its purpose is to help you learn enough to choose your next step.

For me, that is the hopeful part of this moment: people can discover abilities, interests and ways to contribute that they have not had the chance to explore. We should help each other do that. Technology should expand our ability to create a useful, meaningful life.

Next week: Could the AI buildout offer a path beyond desk-based IT? We will look at data-center work, the skills you may already bring and how to explore a move without starting from zero.

Peter Diamandis and the Moonshots conversations have helped shape my optimism about what comes next. These two recent episodes offered useful perspectives on the opportunities explored here:

• Moonshots #304 — AI infrastructure and skilled work
• Moonshots #305 — Building useful solutions and finding new opportunities