Synopsys (NASDAQ:SNPS) used its Investor Day to outline a higher long-term growth outlook, driven by demand for application-specific silicon, AI-enabled engineering workflows and the integration of Ansys simulation technology.
Chief Executive Officer Sassine Ghazi said the company is positioning itself around what it calls “silicon-to-systems” engineering, as customers increasingly need to co-optimize chips, packaging, software and physical-system performance. He said the company holds leading positions in electronic design automation, design intellectual property and simulation and analysis.
Higher long-term growth targets
Synopsys said it expects overall revenue to grow at a mid-teens compound annual rate through fiscal 2030. The outlook includes mid-teens growth in EDA, double-digit growth in simulation and analysis, and high-teens growth in design IP.
For fiscal 2027, the company forecast revenue growth of 15% to $11.15 billion at the midpoint. It expects non-GAAP operating margin to expand 250 basis points to 44%, non-GAAP earnings per share to rise 27% to $19.08, and free cash flow to reach approximately $3.1 billion.
Chief Financial Officer Shelagh Glaser said Synopsys expects operating margin to approach 50% by fiscal 2030, up from its prior target in the mid-40% range. The company also expects EPS and free cash flow to grow in the mid-20% range over the long term.
Synopsys said it intends to return up to 50% of free cash flow to shareholders and announced plans to repurchase $1 billion of its shares over the coming months. The company is targeting stock-based compensation equal to 8% of revenue by fiscal 2030.
Application-optimized IP strategy
A central element of the company’s outlook is its expansion into application-optimized IP, or AOIP. Ghazi described the model as a second “factory” alongside Synopsys’ traditional standard-based IP business.
Under the traditional model, Synopsys develops IP based on industry standards and licenses it broadly. The newer model involves working earlier with customers to customize IP around specific workloads and system requirements. That model includes license fees, customization fees and royalties when customer products enter production.
Ghazi said Synopsys has signed multiyear, multigeneration agreements with compute, ASIC and connectivity leaders. He highlighted a $1 billion license-fee agreement covering multiple generations of Amazon’s Graviton, Trainium and Nitro products. The $1 billion figure excludes customization fees and royalties, he said.
Synopsys projects that AOIP revenue will reach $1 billion by 2030 based on current customer contracts and commitments. The company said it expects royalty revenue eventually to exceed license revenue within the business, though it does not expect royalty revenue from the Amazon-related programs in fiscal 2027.
Ghazi said the broader model can create opportunities across Synopsys’ portfolio, including IP, EDA and simulation tools, because customized systems require analysis of factors such as packaging, thermal performance, stress and cooling.
AI platform and OpenAI partnership
Synopsys also outlined its agentic AI strategy, branded as the Synopsys Agentic AI Platform, or Autopilot. Chief Product Development Officer Shankar Krishnamoorthy said the platform is designed to let long-horizon AI agents perform engineering tasks across chip implementation, verification, analog design and physics simulation.
Krishnamoorthy said the agents can decompose higher-level engineering objectives into tool calls and task-level workflows while relying on Synopsys’ physics-based engines and sign-off tools for validation. He said the company has more than 50 customer engagements involving its agentic portfolio, including work with Intel, MediaTek, Samsung Memory and NVIDIA.
Ghazi said Synopsys sees AI as an expansion opportunity rather than a replacement threat for EDA. While AI models can reason, explore and recommend designs, he said, EDA tools remain necessary to generate chip layouts and validate them against manufacturing and physics requirements.
The company announced a multiyear partnership with OpenAI to develop a specialized Synopsys.ai Copilot model. According to Ghazi, the model will be post-trained using Synopsys tools, agents, workflows and engineering knowledge, while OpenAI provides frontier-model intelligence and reasoning capabilities.
OpenAI President and Chairman Greg Brockman said the partnership is intended to help engineers explore more design choices, optimize design targets and shorten chip-development cycles. Synopsys said customers will be able to access the offering as a service, with monetization involving subscriptions, consumption-based tool usage and outcome-based revenue sharing.
Ansys synergies and reporting changes
Glaser said Synopsys remains on track to achieve $400 million in annualized revenue synergies from its Ansys acquisition by fiscal 2029. The company expects more than $100 million in annualized revenue synergies as it exits fiscal 2027, as multi-physics fusion products begin contributing revenue.
Synopsys also said it expects to complete its targeted $400 million in annualized cost synergies in fiscal 2027, one year ahead of its prior fiscal 2028 target.
Beginning in fiscal 2027, the company will move Ansys’ semiconductor business unit into EDA reporting, while the remaining Ansys operations will be presented within Simulation & Analysis. Design IP reporting will remain unchanged.
About Synopsys (NASDAQ:SNPS)
Synopsys, Inc is a technology company that develops electronic design automation (EDA) software, semiconductor intellectual property (IP), and software tools used to design, verify, and manufacture advanced electronic systems. Its solutions support the development of semiconductors, systems-on-chip, and electronic products across the design cycle, from initial specification and architecture through testing and production.
The company’s offerings include digital and custom integrated-circuit design tools, simulation and verification software, physical design and signoff products, and semiconductor IP such as processor cores, interface technologies, and foundational components.
