The AI Shift That Made Everyone a Builder

When a natural disaster strikes or an unexpected political crisis erupts, one of the first priorities for any global company is confirming where your employees are and reaching them quickly. For years, we paid $60,000 annually to a third-party vendor to manage this critical check-in process. It never fully delivered what it promised.

So a member of our people operations team decided to build her own replacement. Using Claude Code linked directly to Remote’s internal employment dataset, she assembled a fully functional tool in just 3 hours and 17 minutes, for a total cost of $216.

Not long ago, a tool like this would have required contracting a specialized vendor, navigating a lengthy corporate procurement process, and waiting months for implementation. What’s most striking? She isn’t a professional software engineer. She just understood the problem better than any outsider ever could, and now she has the AI tools to turn that understanding into a working solution. I didn’t ask her to take this on, no manager assigned the project. She just saw a gap and fixed it.

And she is far from an outlier at Remote. Over the past year, I’ve watched our HR and finance teams take on significantly higher volume and far greater complexity than ever before—all with the exact same number of staff. This level of productivity gain is only possible because their approach to work has changed completely. Instead of waiting for someone else to build the tools they need to do their jobs better, they’re building those tools themselves.

For most of modern business history, “builder” was a specific job title. You were either an engineer or a developer, and everyone else was just a user of other people’s work. AI has upended that old divide. Today, anyone who can clearly articulate a problem can actually build a working solution for it.

To be clear: Professional engineers are just as critical as they’ve ever been. The big shift is that they are no longer the only people who can create new tools and software. This is a brand new dynamic we’re only just starting to see scale.

Data from Deloitte’s 2026 State of AI report underscores how fast this shift is happening: worker access to AI building tools grew 50% in 2025 alone. The decades-old standard tech org structure—where product writes specs, design creates mockups, and engineering handles all the actual building—is already changing. We’ve seen this play out first-hand at Remote. We built an internal platform that lets any employee build and launch their own tools, and team members across every department have jumped at the opportunity.

A localization specialist built a custom pipeline tool to streamline content workflows across 24 different languages. A product manager built a tool that automatically cross-checks new feature requests against our existing roadmap to cut down on redundant work. I do this too: I built a custom AI agent that runs in Slack, monitors our customer channels, summarizes key discussions, and logs critical customer takeaways for our team. My chief people officer has built multiple custom tools for her team, as have leaders across finance, sales, marketing, and legal. This isn’t a top-down company program or a formal initiative we pushed. Employees just saw what’s now possible with AI, and they started building.

This same shift is reshaping how new startups get off the ground. When you can go from a raw idea to a working product with far less capital and support than was required even a few years ago, launching a new venture becomes accessible to far more people. A single person with a clear vision and the right AI tools can now build something that would have required an entire team and hundreds of thousands of dollars in resources just a few years ago.

My prediction? More people will launch companies earlier in their careers. This isn’t because everyone suddenly wants to be a founder—it’s because the cost of testing an idea has dropped dramatically. The bar for what you need to launch a new business has shifted too. A strong technical team is still make-or-break as you scale, but the initial barrier that killed so many good ideas before they ever got off the ground is far lower today.

We’re already seeing this play out in public data. The share of new startups launched by a solo founder jumped from 23.7% in 2019 to 36.3% by mid-2025, and this growth lines up almost perfectly with the mainstream adoption of AI building tools. More people are freelancing with AI support, launching side projects, and building multiple streams of income than ever before. The companies that emerge from these efforts look different too. When you can build from anywhere, you hire the best person for the role, not the closest person to your headquarters. Teams end up distributed across countries not as a forced policy, but simply because that’s where the best talent for the work lives.

For a long time, execution was the hardest part of building anything. You either had to know how to build it yourself, or you had to find and pay someone who could. That barrier kept countless good ideas from ever seeing the light of day. That’s mostly not the case anymore.

What’s left as the real bottleneck is the far harder work: figuring out what is actually worth building. Good judgment, product taste, and the willingness to take ownership of whether a solution actually works—none of these get automated by AI. In fact, they’re more valuable than ever.

The traditional early career path used to look the same for almost everyone: join the right established company, learn how the business works, slowly work your way up to earn the right to contribute. That path still exists, and it still matters. But now there’s another path available to more people than ever before: find a real problem no one is solving well, and go build something to fix it. That opportunity is open to more people, in more places around the world, than it has ever been in history.