Annie Tsai

When non-technical folks started leveraging AI tools to generate code, someone slapped the term “vibe coding” on top of it and that became an instant viral hit. While it’s meant to describe what non-technical founders and operators are doing when they use Claude, Cursor, Copilot, or Replit to write or debug code, the underlying insinuation is that you’re not building something real – you’re guessing, winging it, maybe hoping the vibes carry you straight through the logic.

This framing ignores the reality inside modern engineering teams, where AI-assisted development isn’t fringe, or experimental, or unofficial. It’s quickly becoming the norm. And the people tossing around the phrase “vibe coding” are missing one crucial truth: the entire industry already builds exactly this way.

This week, we’re tracking what the reporting actually shows about AI-assisted software development, who is using it, and how it’s reshaping both engineering and entrepreneurship.

AI-Assisted Coding Is Now Standard in Engineering

GitHub reports that 1.8 million developers use Copilot daily, and enterprise teams that adopt it see 55% faster development cycles, significantly fewer bugs, and an overall reduction in cognitive overhead. Read more →

This isn’t an outlier. Cursor, the AI-native IDE, has surpassed one million monthly active users, driven not by hobbyists, but by working engineers incorporating AI into their everyday flows. (It’s a great product, btw) Read more →

Even back in 2023, Replit’s Ghostwriter was generating over 30% of the code on the platform, including internal systems used by Replit’s own engineering team. Read more →

Meanwhile, Fortune 500 teams are deploying Claude for SQL debugging, architectural modeling, and very interestingly, refactoring legacy COBOL systems and bloggers are going deep down that rabbit hole – proof that AI-assisted development is already embedded in high-stakes enterprise environments. Read more →

The Real Difference: Pattern Recognition, Not Purity

Part of the confusion here is rooted in an outdated mythology about what “real engineering” is supposed to look like.

We still picture the stereotypical web 1.0 coder: someone in a dark quiet room, typing syntax, building everything from scratch, troubleshooting through sheer will, cursing at punctuation. But that’s not how software gets built today, nor is it how engineering works. Modern engineering is collaborative, pattern-driven, and dependent on shared knowledge: code reviews, escalations, documentation, Slack threads, and now AI tools that expand everyone’s cognitive reach.

A non-technical founder using AI to scaffold an app or debug a finicky rendering issue is not doing something categorically different from a senior engineer using Claude or Copilot to generate tests, propose architectural changes, or outline migration logic. The difference is simply experience – pattern recognition, accumulated over time. You learn the same way every engineer learns: by doing the work, asking questions, breaking things, fixing them, and moving on.

A Real-World Example: How Engineering Actually Happens

Last week, after a long day running operations, finance, people, GTM, and finally getting the kids to sleep, I spent my night debugging a CMS rendering issue for one of my learning projects. The code rendered fine in the development environment but broke in production. I tried six or seven variations, squinted at the DOM, refreshed the environment – but alas, it was late, and my brain was ready to shut off. The next morning, I mentioned it to one of our developers while we were catching up and he offered to take a look. In ten minutes, we were able to walk through the code and he recognized and fixed the issue because he’d seen it before.

That’s engineering. There’s no magic or mythology – this is how the work happens. The team shares knowledge. People ask for help when they’re stuck. We learn, fix, and move on. And AI tools now sit inside that same loop, speeding up the moment when someone gains the clarity they were missing.

Companies Building Faster With AI Development

You can see this across the industry.

Earlier in the year, Retool recently launched AI agents to automate debugging and ops work and announced 100 million hours have already been automated. Read more →

Fast Company just today published a piece on how AI is creating new growth dynamics for companies and the risks it presents to legacy “pre-AI” organizations. Read more →

Salesforce is one of many BigCo tech companies offering advice and products for how AI can speed up your AI development lifecycle. Read more →

There’s even advice today on Forbes about how AI can help small businesses start and grow. Read more →

Your Takeaway This Week: Stop Downplaying Your Work

AI is both changing the tools used to build and grow companies and redefining who gets to do that work. From enterprise engineering teams accelerating through their product roadmaps to individual builders shipping side projects on nights and weekends, the standard has shifted. What used to require years of training and gatekeeping now begins with curiosity, consistency, and the willingness to try, ask for help when you hit the edges of your pattern library, and apply a product manager’s mindset to every problem.

AI-assisted development is how software gets made today and in the future. Not hypothetically. Not someday. Right now. Dismissing your own work as “vibe coding” ignores the truth: you are learning the same way engineers always have – by building, breaking, fixing, iterating, and collaborating. You’re just doing it with better tools. And that’s the point.

Calling your work “vibe coding” sells yourself short because the question now isn’t whether you’re “technical enough” to build, it’s how quickly you’re willing to embrace the new literacy that’s defining modern business.

What problem are you going to solve this week?