Vibe Coding Has Grown Up: What Career Changers Need to Know

The debate has acquired sharper language; for example, in a September 2026 opinion piece for Inc., Joe Procopio used the label "doomcoding" for the problems now creeping into AI-assisted development. The term lands because rapid generation can tempt people into accepting code they don't actually understand, especially when the app seems to work fine.

A Futurism report adds a more tangible warning through an anonymous engineer's account of one company using Claude Code heavily. The engineer describes long days approving AI output while bugs remain unresolved. One workplace account isn't a measure of how software teams everywhere operate, but it raises the right question: can people explain, test and improve what the tool produces? Faster generation has little value if fixes keep piling up.

This doesn't necessarily mean vibe coding has failed; it simply shows what happens when speed outruns scrutiny. Production software still needs:

• Human code review
• Structured testing
• Checks on third-party components and dependencies
• Security assessment
• Proper deployment governance, including confirmation that generated code meets your organisation's standards and legal requirements

Skip those steps and the shortcuts pile up - hardcoded credentials, exposed passwords, unsecured APIs, insecure connections to other services, vulnerable libraries and injection flaws can all hide beneath a polished interface. 

Fixing that security debt later usually costs far more than building carefully in the first place. AI makes it easier to build a first version, but it doesn't make those checks disappear.

What Does This Mean If You're New to Coding?

If you're exploring coding for the first time, vibe coding makes experimentation feel much more accessible. You can build small projects, test ideas and see real results without mastering every tool up front. At Learning People, we see it as a practical way to explore software development, and you don't need to master a programming language before trying a small idea.

That said, our guide to getting into coding explains why the fundamentals still matter. A working knowledge of Python, HTML, CSS or JavaScript helps you question AI output instead of trusting it blindly. Here's a simple way to start:

1. Ask AI to draft a simple app
2. Learn enough coding to follow its logic
3. Change one feature and test what breaks
4. Repeat, with slightly bigger projects each time

Your experience solving problems in another career gives you a head start, while coding fundamentals help you judge whether the result actually works. Practise consistently, take the time to understand how the software works, and let AI speed up your progress rather than replace your judgement.

Build Coding Skills That Keep You in Control

Our coding training can help you build the core knowledge you need to use AI tools with confidence and real purpose, developing the fundamentals alongside practical AI use. And if secure development catches your interest, our cybersecurity training adds hands-on skills in vulnerability assessment, risk and safer deployment, a natural complement to coding.

If you're considering a career change, you may already have the human skills that employers struggle to find, and you may be able to evidence them clearly. Managing deadlines, calming difficult situations, training colleagues and solving problems all demonstrate valuable capabilities. This is also the kind of judgement that AI-assisted coding relies on, since someone still has to decide whether the output is good enough. Technical training can then provide the credentials your target role requires.

Our career pathway training programmes combine recognised technical training with sector-specific AI learning and career support. Explore the route that fits your experience, then build the technical, human and AI skills employers now expect by booking a free career consultation with a Learning People career consultant.