Almost 50 years ago, someone cracked a joke that aged remarkably well:
“The factory of the future will have only two employees - a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment.”
In 2025 this punchline has finally made its way into Electronic Design Automation (EDA). Only now, the “equipment” in question is an AI system with more neural layers than the human brain has excuses for late tape-outs.
Will Circuits Start Writing Themselves?
Recent papers like “Large Language Models for EDA: From Assistants to Agents” (He et al., 2025) and “AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents” (Lu et al., 2025) hint that AI isn’t just helping with design - it’s taking the wheel. Or perhaps, more accurately, re-routing the traces.
AI-driven verification tools (Ravikumar, 2025) and self-aware silicon (Vargas et al., 2025) now promise chips that can debug themselves faster than an engineer can find the semicolon they forgot. Researchers are even generating pseudo circuits at the RTL stage, which sounds suspiciously like AI daydreaming about better hardware.
Meanwhile, CircuitFusion (Fang et al., 2025) teaches chips to learn multimodally - combining circuit diagrams, timing data, and layout specs into one grand, caffeinated neural symphony. Think of it as ChatGPT meets circuit board karaoke.
As Peter Denning observed in Communications of the ACM, AI is both “light and darkness”—a Dickensian tale told in Verilog. Sure, we might get faster chips and fewer bugs, but we might also get less human engineering intuition, replaced by a kind of silicon omniscience that never sleeps and never spills coffee on the FPGA board.
Ray Kurzweil imagines a beautiful merger of human and machine minds. Sohn imagines a utopia. The rest of us? We’re just hoping the dog keeps us from accidentally retraining the wrong model.
Forget the flashy “AI singularity.” The real risk is the automation singularity—a slow, incremental outsourcing of human judgment to the same systems we built to help us. AI systems that prioritize speed, cost-cutting, and surveillance could erode not only our autonomy but also the joy of discovery—the little “Aha!” moments that made engineering fun in the first place.
AI in EDA is neither apocalypse nor utopia - it’s a grand debugging session for humanity’s relationship with technology. We’re learning to co-design not just chips, but the very process of innovation.
So, as the man and the dog look over the humming chip factory of the future, one thing is clear: the dog may still guard the console - but now, it’s also probably wearing an AI-powered collar that runs a lightweight EDA agent.
REFERENCES
https://cacm.acm.org/opinion/three-ai-futures/
Ravikumar S. AI-driven verification: Augmenting engineers in semiconductor EDA workflows. World Journal of Advanced Engineering Technology and Sciences. 2025 May 30;15(2):223-30.
Liu S, Fang W, Lu Y, Zhang Q, Xie Z. Towards Big Data in AI for EDA Research: Generation of New Pseudo Circuits at RTL Stage. InProceedings of the 30th Asia and South Pacific Design Automation Conference 2025 Jan 20 (pp. 527-533).
Mandadi SP. AI-Driven Engineering Productivity in the Semiconductor Industry: A Technological Paradigm Shift. Journal of Computer Science and Technology Studies. 2025 Jul 13;7(7):543-9.
He Z, Pu Y, Wu H, Qiu Y, Qiu T, Yu B. Large Language Models for EDA: From Assistants to Agents. Foundations and Trends® in Electronic Design Automation. 2025 Apr 30;14(4):295-314.
He Z, Yu B. Large language models for eda: Future or mirage?. In Proceedings of the 2024 International Symposium on Physical Design 2024 Mar 12 (pp. 65-66).
Xu Z, Li B, Wang L. Rethinking LLM-Based RTL Code Optimization Via Timing Logic Metamorphosis. arXiv preprint arXiv:2507.16808. 2025 Jul 22.
Mohamed KS. The Basics of EDA Tools for IC: “A Physics-Aware Approach”. InNext Generation EDA Flow: Motivations, Opportunities, Challenges and Future Directions 2025 Apr 12 (pp. 91-129). Cham: Springer Nature Switzerland.
Vargas F, Andjelkovic M, Krstic M, Kar A, Deshwal S, Chauhan YS, Amrouch H, Tille D, Huhn S. Self-Aware Silicon: Enhancing Lifecycle Management with Intelligent Testing and Data Insights. In2025 IEEE European Test Symposium (ETS) 2025 May 26 (pp. 1-10). IEEE.
https://www.linkedin.com/feed/update/urn:li:activity:7356043406298546176/
https://www.linkedin.com/in/sebastian-huhn-84657768/
https://www.linkedin.com/posts/sebastian-huhn-84657768_ieeeets-siliconlifecyclemanagement-testandreliability-activity-7334929170373783552-fuCk
F Vargas, M Andjelkovic, M Krstic, A Kar… - … IEEE European Test …, 2025 - ieeexplore.ieee.org
Fang W, Liu S, Wang J, Xie Z. Circuitfusion: multimodal circuit representation learning for agile chip design. arXiv preprint arXiv:2505.02168. 2025 May 4. https://arxiv.org/pdf/2505.02168
https://github.com/hkust-zhiyao/CircuitFusion
Fang W, Wang J, Lu Y, Liu S, Wu Y, Ma Y, Xie Z. A survey of circuit foundation model: Foundation ai models for vlsi circuit design and eda. arXiv preprint arXiv:2504.03711. 2025 Mar 28.
Lu Y, Au HI, Zhang J, Pan J, Wang Y, Li A, Zhang J, Chen Y. AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents. arXiv preprint arXiv:2508.01012. 2025 Aug 1.
Wei A, Tan H, Suresh T, Mendoza D, Teixeira TS, Wang K, Trippel C, Aiken A. VeriCoder: Enhancing LLM-Based RTL Code Generation through Functional Correctness Validation. arXiv preprint arXiv:2504.15659. 2025 Apr 22.
Next Generation EDA Flow: https://www.google.com/books/edition/Next_Generation_EDA_Flow/
RapidGPT: https://docs.primis.ai/ - industry’s first AI-based pair-designer tailored to ASIC and FPGA engineers
