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OpenAI and Synopsys partner to build "GPT-Synopsys" for autonomous chip design — specialized AI model will operate EDA tools, allowing engineers to deliver more sophisticated chips faster Agentic AI comes to chip design OpenAI and Synopsys have signed a multi-year agreement to jointly develop GPT-Synopsys, a specialized model optimized for semiconductor design using Synopsys's electronic design automation (EDA) tools. According to the September 30 announcement, the partnership “brings together OpenAI's advanced AI capabilities with Synopsys's industry-leading EDA tools and agentic AI capabilities to revolutionize the design of semiconductors.” Engineers would delegate to agents that run the tools and execute the required processes until the work is ready for review. The companies say this would allow design teams to evaluate more options and deliver more sophisticated chips. Under the preferred partner agreement, OpenAI is licensing Synopsys's tools to develop the model — which will run on OpenAI-hosted infrastructure — after which the companies will jointly release the product and share revenue. The announcement did not disclose a release date or a pricing structure. AI in chip design so far Engineers use EDA tools to design and verify chips before manufacturing. This multi-step process begins with writing the hardware description in register-transfer level (RTL) code and using synthesis software to convert it into logic gates. Next, physical design tools lay out the circuit elements and route the connections between them. Lastly, designers close timing, verify functionality, and ensure manufacturing compliance before tapeout for fabrication. Each of these stages requires several iterations — repeatedly running various tools and troubleshooting issues — to balance critical trade-offs: power, performance, and silicon area (PPA). Synopsys, Cadence, and Siemens EDA have dominated this market for these tools long before the current AI boom. Given the technology's capabilities, it is only natural that AI has found its way into the chip design process, creating a sort of “silicon designing silicon” loop. In March 2020, Synopsys launched DSO.ai (Design Space Optimization AI), a reinforcement learning tool that explores and learns from previous design optimizations to improve PPA. The company then launched Synopsys.ai Copilot in November 2023 — its first integration of generative AI — through a collaboration with Microsoft, integrating the Azure OpenAI service to provide natural language assistance within its engineering tools. The company’s current direction is Agentic AI, first revealed at the Design Automation Conference in July, where it showed an autonomous verification workflow built on Nvidia's Agent Toolkit and Nemotron 3 Ultra model. On September 28, two days before the OpenAI deal, Synopsys announced its Autopilot platform and AgentEngineer, a portfolio of seven long-horizon agents covering verification, implementation, analog, manufacturing, meshing, combustion, and EMC analysis. The company reported more than 50 engagements, with availability planned for late 2026. As detailed in our State of agentic AI in chip design tools roadmap, the move to agentic AI is not exclusive to Synopsys; Cadence and Siemens are also pitching autonomous design agents. Meanwhile, OpenAI, in collaboration with Broadcom, unveiled Jalapeño in June, the company’s first custom inference accelerator. OpenAI said the accelerator's design and optimization process leaned heavily on its AI models, allowing it to go from initial design to tape-out in just nine months. Now, GPT-Synopsys turns that internal experiment into a product. GPT-Synopsys According to the announcement, GPT-Synopsys will combine OpenAI's frontier models with Synopsys’s EDA software and domain expertise, allowing the model to reason about chip design and verification and operate Synopsys tools directly. Engineers would hand the model objectives such as PPA optimization, timing, and verification closure, while the agents run the tools, interpret the results, implement changes, and iterate toward verified outcomes for an engineer to review. In other words, the model would be capable of making engineering judgments involved in using Synopsys software. The companies describe it as learning to operate the tools like an expert engineer, interpreting their outputs and using the results to guide further changes. This is the proposed specialization that takes the model beyond connecting a general-purpose model to a set of tools. Synopsys’s EDA engines would perform the calculations and checks, with the model interpreting results and the agent software managing execution. GPT-Synopsys will run on OpenAI-hosted infrastructure and is intended to integrate with Synopsys.ai and Autopilot, as well as work with customers’ own agent harnesses. Autopilot already provides services such as memory and governance. Synopsys says early technology engagements are underway with leading semiconductor customers, but didn't name any. The immediate audience is professional semiconductor-design teams, with companies developing their own custom silicon also likely to use it. Although the announcement covers semiconductor design generally, OpenAI has a particular interest in improving the hardware that runs its models. “By helping them build better chips, we can build better AI and bring it to more people,” said Greg Brockman, OpenAI’s president and co-founder. Whether the service also makes advanced design more accessible to smaller teams will depend on pricing and the expertise still required to supervise it. Questions and concerns The recent announcement leaves a couple of questions and concerns unaddressed. First, hosting the model on OpenAI infrastructure raises concerns about how confidential designs are handled. The companies say customer data will be excluded from model training, encrypted at rest and in transit, and governed by configurable retention, audit, and permission controls. However, the release does not identify hosting regions or default retention periods. It also provides no contractual terms for ownership of generated outputs, use of third-party licensed design IP, or indemnities. The service's terms will likely address these questions. Another question is the practical economics for users. The industry has realized that handing over everything to AI doesn't always result in cost savings, at least for now. Sometimes the reverse is the case. Earlier this year, Uber’s CTO and an Nvidia executive said AI was more expensive than human workers, although many companies are reportedly fine with the extra cost. For GPT-Synopsys, advanced engineering reasoning and faster design-to-tapeout could justify the service, but model calls, EDA runs, integration, and human review all consume resources. The announcement did not include a pricing structure. It also didn't specify a launch date. Meanwhile, Synopsys’s main rival Cadence launched its ChipStack AI Super Agent in February for front-end design and verification, built on frontier LLMs. In April, the company announced a collaboration with Google to optimize ChipStack with Gemini on Google Cloud. At Computex, it extended ChipStack to what it calls Level-5 autonomy, powered by Nvidia's Nemotron models, with early access expected in the second half of 2026. The main difference is that while ChipStack is Cadence’s own agent software running on other companies’ general-purpose models, GPT-Synopsys will be an OpenAI model specifically trained to operate Synopsys’s tools. Etiido Uko is a news contributor for Tom's Hardware covering the latest updates in big tech and the PC industry. He is a mechanical engineer and senior technical writer with over nine years of experience in documentation and reporting. He is deeply passionate about all things engineering and technology, and is an expert in gadgets, manufacturing, robotics, automotive, and aerospace.
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