The Validation Debt: As AI Coding Surges, Software Verification Emerges As The New Bottleneck - Mudit Singh

Agentic AI may have dramatically accelerated software development, but an emerging problem is forcing companies to quietly abandon AI initiatives: proving that AI-generated systems actually work as intended.

Across the industry, executives are noticing a structural shift. The bottleneck in software development has moved from writing code to validating it, creating a new operational challenge that is increasingly dictating enterprise AI investments.

"For most of software's history, writing the code was the bottleneck. AI has automated that part. What hasn't kept pace is proving that the software actually works," said Mudit Singh, Co-founder and Head of Growth at TestMu AI (formerly LambdaTest). "That is not a productivity gain. It is a transfer. You have moved risk from the moment of writing to a later stage where failures are discovered."

Singh describes this phenomenon as "validation debt”, the growing gap between the pace at which AI generates software and an organisation's ability to verify its correctness.

As a platform that sits within the testing workflows of thousands of engineering teams, TestMu AI has a unique vantage point on this crisis. According to Singh, leaders across various industries are echoing the same concern: they are shipping software faster than they can verify it.

"We keep hearing the same concern from engineering & quality leaders across industries: they were shipping software faster than they could verify it. When unrelated teams describe the same problem, it's structural," he said.

The issue is becoming even more pronounced as enterprises move beyond simple AI copilots to autonomous AI agents capable of executing multi-step workflows and interacting with other systems without human intervention.

"Traditionally, a software bug lives somewhere, a function, a service, or a line of code," Singh explains. "When autonomous agents interact, failures often emerge from those interactions rather than from any individual component. Neither agent may be broken. The conversation between them is."

That complexity is actively reshaping how software quality is measured. Simply using AI to test AI is insufficient. The question, Singh notes, is no longer whether AI can write tests; it can, but whether an automated "green check" can be trusted to gate or block a release.

This industry-wide gap has driven the development of new paradigms in quality engineering, positioning specification, rather than code, as the primary asset being verified. For example, TestMu AI introduced Kane AI, a test agent for planning and authoring that generates and maintains tests from plain-language descriptions rather than hand-written scripts.

Similarly, the demand for auditability is rising. Tools like TestMu’s Kane CLI are emerging to produce concrete evidence trails of what an automated test attempted and the reasoning behind its conclusion. This allows human reviewers to audit the AI's decision rather than blindly accepting an unexplained result.

"We built the evidence trail because customers kept encountering the same wall," Singh says. "Their own risk and compliance teams would ask why an automated system had approved something, and there was no answer available. That is a governance problem before it is an engineering one. We are early in solving it, and so is the rest of the industry."

This governance challenge is beginning to shape how organisations think about their AI investments. Where teams cannot clearly demonstrate that systems consistently behave as intended, confidence in scaling those systems can be harder to sustain. More broadly, this shift is changing the expectations placed on software developers.

"If AI increasingly writes the implementation, then the specification becomes the most important engineering asset. The spec you give the agent is the source of truth," Singh says. "Write it clearly, and the agent has exact functional goals and architectural guardrails. Write it vaguely, and you get software that looks right and behaves wrong. That can be more dangerous than code that visibly breaks."

Despite ongoing anxieties about AI's impact on tech employment, Singh remains optimistic about the future, particularly for India’s massive software testing and quality engineering workforce. Rather than jobs disappearing, he says the work is moving up the value chain, with the real constraint being how quickly professionals can retrain to manage AI agents.

These themes will take center stage at the upcoming TestMu Conference 2026 (August 19-21), where over 80 industry leaders will debate the future of AI-driven quality engineering. As Singh summarises, "The industry has spent the past two years asking whether AI can write software. The next phase is about whether organisations can reliably prove that the software works."