Synopsys Raises Annual Forecasts as AI Complexity Drives Chip Design Software Demand

Synopsys Reports Strong Q3 Results and Raises Annual Forecasts

Synopsys posted robust third-quarter results with revenue growing 42% year-over-year to $2.48 billion, exceeding the analyst consensus estimate of $2.44 billion3

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. The company delivered adjusted EPS of $3.91, beating the Street estimate of $3.673

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. Following these results, Synopsys raised its fiscal year 2026 adjusted EPS guidance to $15.04-$15.10 from $14.72-$14.80, and revenue guidance to $9.69 billion-$9.74 billion from $9.625 billion-$9.705 billion3

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. The upside was driven by contribution from the Ansys acquisition and outperformance in Design Automation2

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. CEO Sassine Ghazi stated that AI is creating unprecedented complexity and driving stronger demand for the silicon IP and engineering solutions needed to build next-generation AI computing infrastructure3

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AI Complexity Fuels Demand Across Electronic Design Automation and Design IP

The AI boom is driving investments in chips and infrastructure, boosting demand for Synopsys' chip design software1

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. Electronic Design Automation revenue grew 8.5% year-over-year, driven by strong software performance and a record quarter for hardware-assisted verification3

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. The company added 12 new and 66 repeat hardware-assisted verification customers in the third quarter3

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. Design IP revenue grew 11% to $474 million, with the business winning more than 95% of PCIe 7 opportunities and securing 25 LPDDR6 design wins year to date3

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. Die-to-die IP revenue is on track to double year over year, with more than 100 design wins3

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. USB IP Lifetime Bookings crossed $2 billion, with tier-one wins advancing to leading-edge nodes3

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. Synopsys maintained a 90%+ automotive design win rate for three straight quarters as ADAS platforms transition to 5nm and 3nm technologies3

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Ansys Acquisition Performs Ahead of Expectations

Ansys contributed approximately $711 million in revenue during the quarter, and management indicated that the acquisition is tracking ahead of cost synergy expectations at the time of the deal2

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. For Ansys, semiconductors, aerospace and industrial sectors were particularly strong as these industries continued to upgrade their digital engineering capabilities2

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. Synopsys projects Ansys revenue contribution of $2.98 billion for the full year3

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. The updated revenue guidance for the full year reflects 38% growth, with Ansys growing 9.5% in the fourth quarter2

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. The company's new Multiphysics Fusion solution delivered up to 10x faster design closure and 3x faster runtime in customer validation, with revenue contributions expected from 20273

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Agentic AI Workflows Deliver Measurable Productivity Gains

Synopsys has over 30 agentic AI engagements, with its verification agent achieving up to 50x faster validated RTL, 20% better coverage and up to 40% shorter debug cycles3

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. Chief Product Development Officer Shankar Krishnamoorthy revealed that one customer is observing a 5x-6x productivity gain using the company's agentic flow for formal verification5

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. The AI-driven workflow also identified a bug pointing to a persistent modeling issue that traditional tools had not identified5

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. Early evaluations of the autonomous debugging workflow, developed in collaboration with Microsoft Discovery, showed reductions of 25%-40% in debug cycle time, saving many weeks of engineering efforts5

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. The end-to-end autonomous verification workflow compresses weeks of manual labor into hours of agentic execution5

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Autonomous Engineering Represents the Next Phase Beyond AI Assistants

Krishnamoorthy believes the AI industry is transitioning from AI-assisted to autonomous engineering, where AI will orchestrate end-to-end engineering workflows across design, verification, simulation, and system validation4

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. Organizations are moving beyond point solutions and task-specific AI assistants toward autonomous engineering workflows that can help manage complexity at scale4

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. According to Krishnamoorthy, companies that successfully combine agentic AI, accelerated computing, and multiphysics simulation will be best positioned to improve engineering velocity, deliver first-time right silicon, and advance innovation4

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. Rather than replacing engineers, AI is enabling them to focus on higher-value work by automating complex tasks and exploring more design alternatives before a chip reaches production5

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. Analysts noted that the results validated that AI is a tailwind to EDA and simulation software, rather than a headwind2

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. Watch how quickly these productivity gains become standard across the semiconductor industry and whether autonomous engineering workflows begin reshaping competitive dynamics in chip design.