Much of the conversation around artificial intelligence still revolves around what it could do for chip design. Synopsys, Inc. (NASDAQ:SNPS) says that the future may already be here.
In an exclusive email interview with Benzinga, Synopsys Chief Product Development Officer Shankar Krishnamoorthy shared rare quantitative evidence of AI’s real-world impact, saying customers are already reporting productivity gains of up to six times while autonomous engineering workflows are reducing tasks that once took weeks to just hours.
• What should traders watch with SNPS?
Synopsys Says Agentic AI Is Delivering Measurable Productivity Gains
Technology companies often describe AI in broad terms, but Synopsys backed its claims with specific performance metrics.
Krishnamoorthy said, “One of our customers is observing a 5x-6x productivity gain using our agentic flow for formal verification.”
Beyond accelerating work, the AI-driven workflow also improved outcomes by identifying “a bug, which pointed to a persistent modeling issue that traditional tools had not identified,” added Krishnamoorthy.
The findings suggest AI is doing more than automating repetitive engineering tasks. It is also helping engineers uncover problems that conventional verification methods can miss, potentially reducing costly design iterations later in the development process.
That shift is becoming increasingly important as semiconductor designs grow more complex and verification consumes a larger share of engineering time.
AI Is Compressing Weeks of Engineering Into Hours
Synopsys also pointed to early results from its autonomous debugging workflow, developed in collaboration with Microsoft Discovery.
According to Krishnamoorthy, early evaluations have shown “reductions of 25%-40% in debug cycle time,” “saving many weeks of engineering efforts and improving productivity.”
He added that the company’s end-to-end autonomous verification workflow “compresses weeks of manual labor into hours of agentic execution,” helping address one of the industry’s biggest engineering bottlenecks.
Rather than replacing engineers, Krishnamoorthy said AI is enabling them to focus on higher-value work by automating complex tasks, orchestrating end-to-end workflows and exploring more design alternatives before a chip reaches production.
The broader implication is that AI’s value may ultimately be measured less by how quickly it generates code and more by how much engineering time it eliminates across the product development cycle.
Why Investors Should Watch Productivity, Not Just AI Adoption
As AI spending accelerates across the semiconductor industry, investors are increasingly asking whether those investments are producing measurable returns.
Synopsys’ customer examples offer an early answer. Instead of discussing AI as a future productivity tool, the company says customers are already reducing debug cycles by as much as 40%, completing engineering workflows in hours rather than weeks and achieving productivity gains of up to six times.
For investors, the next milestone to watch is whether these early results become commonplace across the semiconductor industry. Synopsys is scheduled to report earnings after the market closes Wednesday, giving investors a timely opportunity to assess whether demand for its AI-enabled design and verification tools is translating into broader financial momentum.
If autonomous engineering continues to deliver measurable improvements in productivity, quality and time-to-market, AI could become as important to designing the next generation of chips as it has been to powering them.
Photo: Shankar Krishnamoorthy, courtesy Synopsys Inc
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