Earlier in my IT career, I wished there were a way to accelerate experience—to encounter years' worth of real problems, see the mistakes people made, understand why they failed, and learn how they eventually solved them. Experience comes quickly when you repeatedly encounter problems and have to overcome them.

AI changes what is possible here, particularly in software development.

An AI coding agent can now accumulate experience through the work itself. It attempts a solution, encounters a problem, diagnoses it, tries different approaches, and eventually finds what works. That process produces something potentially more valuable than the finished code: a lesson.

And we aren't starting from zero. There is already a massive world of GitHub repositories containing working solutions to real problems. When starting a new project, finding the right repository can provide an enormous head start. AI makes this even more useful because it can reference another project's code and approach while working through your own problem, extracting relevant patterns without requiring you to understand the entire project first.

The next step is to extend that idea beyond the code itself.

The lessons generated during an AI-assisted build don't necessarily require exposing the underlying codebase. They could be abstracted and anonymized into the problem encountered, approaches that failed, why they failed, the eventual solution, and what should be avoided in the future.

Now imagine those lessons becoming universally referenceable.

Instead of every AI encountering the same problems independently, one AI's experience could become available to others. The accumulated mistakes and solutions from millions of builds could form a growing body of practical experience that future AI systems can reference while working.

That creates a fundamentally different learning cycle:

Encounter a problem → attempt solutions → discover what works → abstract the lesson → share it → reference it during future builds.

The important shift isn't simply that AI can write code. It's that AI can do the work, experience the failures, extract the lessons, and potentially make those lessons reusable without exposing the proprietary work that produced them.

If that becomes possible at scale, experience itself becomes something we can accumulate and share much faster—allowing each new build to benefit from mistakes already made somewhere else rather than repeatedly rediscovering them.