"In 2026, generative AI stops being an experiment for software development and starts being an architectural liability. The initial rush to apply AI everywhere is hardening into a struggle with execution, where the primary hurdles are no longer capability, but control, cost, and security. We are already seeing the cracks in code integrity. As AI-assisted development becomes standard, the volume of code produced is outpacing human capacity to audit it. This "vibe coding" prioritises speed over structural soundness, creating a new category of technical debt."
"Shaun Cooney, CPTO at Promon, puts a number on the danger: "By 2027, as much as 30 percent of new security exposures may stem from vibe-coded logic." Cooney warns that this rapid production model degrades established quality checks. "The rapid development model enabled by AI-generated code often bypasses traditional guardrails such as manual review, static analysis, and structured quality assurance.""
"This opacity extends to the software supply chain. Martin Reynolds, Field CTO at Harness, points out that AI tools often obscure the origin of the code they suggest. "AI-generated code also typically lacks clear provenance; developers can't trace where suggestions originated or whether they incorporate licensed code or vulnerable components." Because these tools train on historical repositories, they often lack awareness of real-time vulnerabilities. Consequently, they "will happily draw from vulnerable libraries,""
Generative AI adoption in software is shifting from experimentation to an architectural liability where control, cost, and security become primary constraints. Rapid AI-assisted code production outpaces human auditing capacity, producing "vibe coding" that prioritises speed over structural integrity and creates new technical debt. Experts warn that a substantial share of future security exposures may stem from machine-generated logic and that AI-enabled workflows can bypass manual review, static analysis, and structured QA. AI code suggestions often lack provenance and can draw from vulnerable, historical libraries, widening competence gaps and impairing software supply chain visibility. Runtime environments are also fragmenting into active agents, altering infrastructure complexity.
Read at Developer Tech News
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