As AI enters semiconductor verification, it is erasing roles for beginner training while making expert human judgment more critical than ever. How?
Artificial intelligence (AI) is rapidly transforming semiconductor verification, helping engineers automate scripting, generate tests, trace coverage gaps, and modify large codebases in minutes instead of days. But as chip companies race to integrate AI deeper into their workflows, one problem refuses to disappear. When a faulty processor reaches production, no AI model takes responsibility for the failure. The accountability still falls on the engineer who approved the design.
That contradiction is what semiconductor verification veteran Amit Chaurasiya, Senior Staff and Manager, verification at Qualcomm, calls the “sign off paradox.” In an exclusive interaction, Amit explained that the semiconductor industry is increasingly depending on AI at a time when chip complexity is exploding. For nearly two decades, he has worked on verification programs at companies including Broadcom, Qualcomm, and Innatera Nanosystems, watching processors evolve from millions of transistors to hundreds of billions inside modern AI accelerators.
Verification is the final checkpoint before a chip design moves to fabrication, or tape out. Engineers spend months stress testing designs, simulating edge cases, tracing bugs, and deciding whether silicon is truly ready for production. If a critical functional bug escapes into manufacturing, the fallout can include delayed launches, failed deployments, recalls, and losses worth hundreds of millions of dollars.
According to Amit, AI is already highly effective at repetitive engineering work such as scripting, regression management, coverage analysis, and large-scale code modifications. The limitation, however, is that AI still lacks a deep architectural understanding of real-world hardware systems.
“AI understands syntax extremely well,” he said. “It can generate SystemVerilog code that compiles. But architecture-level reasoning and debugging judgment are very different things.”
Unlike software AI systems trained on massive public repositories, semiconductor AI models have limited exposure to real production hardware designs because chip architectures remain among the most closely guarded assets in the technology industry.
Amit compares the situation to ADAS (advanced driver-assistance system)-enabled cars, where software assists the driver but accountability still remains human.
“In an ADAS-enabled car, radar and cameras help the driver, but if an accident happens, the driver is still responsible,” he explained. “The same applies to chip verification. AI can assist engineers, but no AI company will ever take responsibility for a silicon failure.”
In practice, engineers still need to validate every critical AI generated verification decision. Because when billions of dollars are riding on a tape out decision, accountability cannot be outsourced to an algorithm.
