When the Tokens Run Out: The Hidden Cost of AI Vibe Coding

For the past few years, AI assisted software development has been pitched as a straightforward win. Faster delivery. Fewer bugs. Smaller teams achieving bigger results.  Somewhere along the way, that optimism hardened into belief.  Developers were told they were witnessing the end of coding as they knew it.

And then the tokens started running out.

What many now call AI vibe coding began innocently enough.  Every modern integrated development environment, the place where code is written, compiled and run, has long supported extensions, terminal access and remote connections to cloud or physical servers.  This is the cockpit of modern software development.

Over the past four years, that cockpit got a co-pilot.

It started with Microsoft Copilot inside Visual Studio, offering inline suggestions and light code review. Within what felt like weeks, third party IDEs pushed the concept much further. Tools like Windsurf did not just suggest code; they responded to prompts, ran terminal commands, installed dependencies, created folder structures and stitched together entire projects through a chat window.

That was the birth of vibe coding, a more hands off, prompt driven approach where the developer becomes less a typist and more a conductor. Give the right instruction and the system does the rest.  For a short spell, it felt as though software development had finally been solved.  Except the code was a bit rough around the edges.

Those early weeks told the story.  Experienced developers quickly noticed that although the output looked plausible, it was not always reliable. Bugs reappeared after being “fixed”. The same solutions were applied repeatedly as context slipped away.  Architectural shortcuts crept in, sometimes introducing security risk, just to get past an error. The models were solving problems without necessarily remembering why the problem existed in the first place.

This was not a prompting issue. It was structural.

AI proved brilliant at removing 60 to 80 percent of the initial setup effort.  Greenfield projects came to life in minutes.  But as systems edged towards production, reality drifted away from the documentation. Models referenced menus that no longer existed, commands that had changed, APIs that behaved differently in the real world.  Even when models gained browsing capability, the underlying documentation was often outdated or incomplete.

To compensate, the industry leaned into agent-based workflows.  Smaller chunks. Parallel agents.  Retry loops.  Tools like Claude Autocoder, and what some call the Ralph Wiggum method, tried to manage context loss through decomposition. In theory, it was Agile reborn for AI.

In practice, it introduced a new constraint: token economics.

Dividing work into smaller tasks meant the same context had to be loaded repeatedly.  Multiple agents consumed identical instructions in parallel. Communication between them added overhead rather than efficiency.  What once might have been a month of steady usage shrank to days, sometimes hours. Anyone using Claude knows the moment.  Welcome to a Max subscription.

Tokens that once felt plentiful suddenly became finite.  And when you hit that wall, development does not just slow down. It fractures.

From my perspective at SevTech, the response required a change in mindset.  I stopped thinking in terms of a Minimum Viable Product and started focusing on a Minimum Viable Context.  Define the golden finish line.  Then strip it back relentlessly. Home page. Login page. Core logic. Treat each phase as its own bounded context, almost like an individual project.  Smaller context windows. Lower burn rates. Fewer hallucinations. Trust, gradually, returns.

But vigilance is essential.  Models fail.  Providers release updates without enterprise grade testing.  APIs buckle under their own hype.  Access to the tools themselves cannot be taken for granted.

This volatility is not accidental.  Every six weeks or so, we see a significant leap in capability, closely followed by marketing hype, excitement and new dependencies. I half jokingly call this "Seavers Law".  The trouble is that rapid improvement is often mistaken for stability.  Enterprises design delivery models that assume AI access will always be cheap, available and predictable.

That assumption has yet to face real scrutiny, not from regulators, not from auditors and not from enough production incidents.

Knowing that tomatoes are fruit does not mean they belong in a fruit salad.  Similarly, just because AI can generate software does not mean it is enterprise ready. Will the application pass a security audit? Will customer data remain protected? Is it compliant with local regulation?  Too often, those questions are answered afterwards.

The hype train encourages trust before understanding. And now, whether we like it or not, we are all passengers.  The destination remains uncertain, but the market is captive. The token wall is the first real gap in the tracks, and it is an expensive one.

So what does responsible leadership look like from here?

Unfashionably, the answer might be Agile, not the buzzword version, the genuine one.  The ability to change direction quickly.  Do not lock yourself into a single provider. Assume the landscape will shift every six weeks.  When tokens expire, have experienced developers ready to continue without AI.  Keep codebases branched and secure, and only release builds that are genuinely stable.  Run AI generated code through the same tools you always have: Snyk, SonarQube, internal reviews and external audits.

Treat agents like junior developers: useful, quick, inconsistent and in need of supervision.

Industries are cyclical.  Trains get delayed.  Tomatoes are fruit.  Access to knowledge does not make us wise old wizards.  How we use that knowledge matters far more than how quickly we acquire it.

AI can absolutely help.  Sometimes the real victory is not code at all, but a process change.  Train your people.  Trust your experts.  Let AI accelerate the journey, but keep humans firmly in charge of the direction of travel.