When Marc Andreessen said, “software is eating the world,” few imagined that software would be written – and then rewritten – by AI. Today, AI accelerates how we build, but not necessarily how we build well. That’s where a new kind of technical debt begins.
In 2024, developers generated over 256 billion lines of code using the best AI tools available. That number is likely to double this year. GenAI has become indispensable, with Microsoft recently noting that 30% of their code is AI-written and growing. It’s helping developers write, test and refactor code at a pace that would’ve been unthinkable just a few years ago.
Beneath this productivity boom lies an uncomfortable truth: AI isn’t just solving technical debt, it’s creating it, at scale.
Founder and CEO of TurinTech.
Vibe Coding: Fast, Fluid, but Fraught
We’ve entered the era of “vibe coding.” Developers prompt an LLM, scan the suggestions, and stitch together working solutions – often without fully understanding what’s under the hood. It’s fast and frictionless, but dangerously opaque.
This new breed of code might appear functional, but too often, it fails in production. Key engineering disciplines – like architectural planning, runtime benchmarks, and rigorous testing – are frequently skipped or delayed.
The result: a wave of unvalidated, non-performant code flooding enterprise systems. GenAI isn’t just a productivity tool. It’s a new abstraction layer, one that hides engineering complexity while introducing familiar risks.
The Paradox of AI Tech Debt
Ironically, AI is also helping tackle legacy tech debt: cleaning up outdated code, flagging inefficiencies, and easing modernization. In that sense, it’s a valuable ally.
But here’s the paradox: as AI solves old problems, it’s generating new ones.
Many models lack enterprise context. They don’t account for infrastructure, compliance, or business logic. They can’t reason about real-world performance and rarely validate outputs unless prompted, and few developers have time or tooling to enforce this.
The result? A new wave of hidden inefficiencies, bloated compute usage, unstable code paths, and brittle integrations – all delivered at speed.
Productivity Isn’t Enough: Viability is the New Standard
Shipping code fast no longer guarantees an edge. What matters now is viability: can the code scale, adapt and survive over time?
Too much GenAI output is focused on getting from zero to anything. Enterprise code must work in context – under pressure, at scale, and without incurring hidden costs. Teams need systems that validate not just correctness, but performance. This means reintroducing engineering rigor, even as generation speeds up.
Viability has become the new benchmark. And it demands a shift in mindset, from fast code to fit code.
A Return to Engineering Fundamentals
This shift is prompting a quiet return to data science fundamentals. While LLMs generate code from natural language, it is validation, testing and benchmarking that determine whether code is production-ready.
There’s renewed focus on engineered prompts, contextual constraints, scoring models which evaluate outputs and continuous refinement. Enterprises are realizing that GenAI alone isn’t enough – they need systems to subject AI outputs to real-world scrutiny at speed and scale.
A New Discipline in AI-Powered Software Development
GenAI has changed how we produce software, but not how we validate it. We’re entering a new phase that demands more than fast code. What’s needed now is a way to evaluate outputs across competing goals—performance, cost, maintainability, and scalability—and decide what’s right for the real world, not just the test case.
This isn’t just prompting better or returning to old data science playbooks. It’s a new kind of AI-native engineering—where systems integrate scoring, benchmarking, human feedback and statistical reasoning to guide outputs toward viability.
The ability to evolve, test and refine AI outputs at scale will define the next wave of innovation.
What’s at Stake
Ignoring this shift comes at a cost: higher cloud bills, unstable code in production, and slower delivery due to rework and debugging. Worst of all, innovation slows—not because teams lack ideas, but because they’re buried under AI-generated inefficiencies.
To fully benefit from AI in software development, we must move beyond the vibe and focus on viability. The future belongs to those who can generate fast and validate faster. Teams that succeed will interrogate their AI-assisted outputs with engineering-grade scrutiny, weighing not just what AI can generate, but using their expert judgment on whether it’s right for the job.
We list the best Large Language Models (LLMs) for coding.
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