Others point to two types of seniority, with one more easily replaced than the other. \u201cOne is a senior engineer who understands lots of the problems from the very bottom to the upper level, which means knowing how to use the tools,\u201d observed Kexun Zhang, head of research at ChipAgents<\/a>. \u201cThe other type has experience about the bigger picture, about how a project is organized, and that kind of experience is gained from years of being in the field, of working together, of succeeding and failing. The first type of seniority, which is about familiarity with a lot of bottom-level tools, is not the most important thing. In computer science (CS) and electronic engineering (EE), we\u2019ve seen lots of generations of tools being invented, and usually the next generation of tools is at a higher level of abstraction than the previous level of tools. When the higher abstraction tool is mature and is fully adopted, even in schools, people don\u2019t really need to know that much detail about the lower level of abstraction. That is true for EE. That is true for CS.\u201d<\/p>\n Existing tools at a lower level of abstraction may not be needed for an engineer\u2019s education, but there is still value in becoming proficient on those tools. \u201cOf course, we still need people to know all these different levels of abstraction, but we don\u2019t need that many junior engineers to go deep into the abstraction,\u201d Zhang said. \u201cThey just need to be at the right level, and still, they can work on the same things and gain experience. They can still become senior engineers.\u201d<\/p>\n This solves the problem of how engineers gain expertise if AI takes many of today\u2019s junior jobs. \u201cThis is a topic of conversation with me and my friends, and basically our whole company about recent grads,\u201d said Daniel Rose, founding AI engineer at ChipAgents. \u201cThere are a lot of people who have been PhD, Master\u2019s, or undergrad students, and all of us are using these amazing advancements of AI to help us code more efficiently and help impact the industry. Otherwise, we would have to spend 10 years to develop to a senior position. AI is helping us impact industries much more quickly.\u201d<\/p>\n In fact, mid-level engineers may find the AI-driven job shift the hardest. \u201cEntry-level engineers will be very used to using AI tools, and they are on the learning curve where they understand aspects of it,\u201d said Nandan Nayampally, chief commercial officer at Baya Systems<\/a>. \u201cThere are senior members who understand a lot more, and have more experience from a system perspective, design flow perspective, and domain expertise perspective, and who have a much bigger understanding of context. There is a section in between that will find using AI a bit challenging. What AI does is move them effectively and faster up that cycle of understanding. AI may be the tools that are needed for gaining that expertise. It\u2019s finally a tool. How you use it best is up to you.\u201d<\/p>\n Nvidia CEO Jensen Huang has repeatedly said, \u201cYou\u2019re not going to lose your job to AI \u2014 you\u2019re going to lose your job to somebody who uses AI.\u2019\u201d And if industry pundits are correct, electrical engineers using AI will replace electrical engineers not using it.<\/p>\n \u201cIt\u2019s just another tool that\u2019s been added to the toolbox to create and allow things to happen,\u201d said Marc Swinnen, director of product marketing at Synopsys<\/a>. \u201cIf you don\u2019t keep up with that, you will not be able to do leading-edge design. For instance, there will always be a place \u2014 and there still is to this day \u2014 for manual analog design. It\u2019s not like one completely makes the other extinct. But the bulk of the market moves to the new paradigm.\u201d<\/p>\n Some jobs will be taken by automation and robots, but new technology also will create more jobs, as it did with the advent of the internet. \u201cI\u2019m optimistic about that, but compared to something like the Industrial Revolution, the only thing I\u2019m worried is the pace is much higher now,\u201d said Ransalu Senanayake, assistant professor in the School of Computing and Augmented Intelligence at Arizona State University, and director of the Laboratory for Learning Evaluation and Naturalization of Systems (LENS Lab<\/a>). \u201cIn the Industrial Revolution, we had pretty much a generation to adapt through this drift. But language models are improving every week, and the same thing with robots, so people need to adapt very quickly. Considering human limitations, I don\u2019t know if that is a possibility.\u201d<\/p>\n CS\/EE\/ECE job market trends \u201cI can\u2019t solve Schrodinger\u2019s equation and I don\u2019t know how to crawl around on my hands and knees and lay out a chip with masking tape on a floor, but there absolutely is a set of skills that will no longer be required to do chip design,\u201d said Matthew Graham, senior group director, verification software product management at Cadence<\/a>. \u201cWhat skills will be required is still TBD in an AI-driven future, in the same way that in the 1920s and 1930s, to be able to drive a car you needed to understand things like spark advance, and you needed to know how to be able to refill the radiator halfway through your trip. Most people who drive cars now couldn\u2019t find the radiator cap if they were paid to, and that\u2019s fine. We haven\u2019t devolved as a society. We\u2019ve evolved. The solution has evolved to the point where you don\u2019t need to know how to do that. That 1920s car driver couldn\u2019t figure out how to use Apple CarPlay. We\u2019ve just migrated the skills. The same thing will happen in chip design with AI. We will migrate the skills.\u201d<\/p>\n Fundamentally, AI is designed to boost human productivity and help tackle design complexity. \u201cIn this vein, it will accelerate products going to market,\u201d said Anand Thiruvengadam, product management senior director at Synopsys. \u201cGiven the significant talent shortage in the semiconductor industry, AI is more likely to help address the productivity bottlenecks than replace human engineers.\u201d<\/p>\n According to Thiruvengadam, trends in the job market include:<\/p>\n Automation of routine tasks: AI tools are increasingly capable of handling routine, repetitive, and lower-complexity coding and design tasks. Examples include generating simple code snippets, automating layout design, or creating basic graphics. Agentic AI to train people faster \u201cThe real value of AI is to have a system that can capture the knowledge and experience of a human and replicate that task as an expert,\u201d said David Fritz, vice-president of hybrid-physical and virtual systems, automotive and mil-aero, at Siemens EDA<\/a>. \u201cWe\u2019re seeing that in medicine and in a lot of things. It\u2019s coming to engineering, and it\u2019s not going to be overnight. It\u2019s going to take time, because putting the knowledge of a group of experts into an artificial intelligence system that is tasked with producing the same quality results is very difficult to verify, time-consuming, and expensive to do the training and the verification.\u201d<\/p>\n Fritz believes that eventually, some software design, hardware design, and system design will be replaced by AI. His recommendation for electrical engineers: \u201cGet up to speed on AI.\u201d<\/p>\n Further, agentic AI tools can serve as an assistant, like an intern or a fresh grad. \u201cThe workforce that\u2019s going to come into the industry is going to be much more trained,\u201d said Sathishkumar Balasubramanian, head of products at Siemens EDA. \u201cI don\u2019t need to waste experienced engineers to train that new person. He\u2019ll be able to learn on his own. He\u2019ll be able to understand how someone else has done the work in a much easier way, like having a professor.\u201d<\/p>\n That would amount to a foundational shift for engineers. \u201cThe era of passive software is over, where I just throw you software and your manual, you have a set of scripts you run, then you go,\u201d said Balasubramanian. \u201cYou first understand how to operate the tool, then you understand how to script it, how to do it, import your data, and do all the stuff you need for analysis. Then you learn it, and do your real project. You still keep learning the tool, rather than solving your real problem, which is making better designs.\u201d<\/p>\n Natural language makes learning easier than wading through manuals. \u201cLike ChatGPT 5, you can interact with it all the time, and then it can help you with setup,\u201d said Balasubramanian. \u201cIt can help you with analysis, debug, and it can even ask you questions. You can ask questions like how to solve this problem.\u201d<\/p>\n At the same time, many are cautious when it comes to agentic AI and large language models. \u201cThey are not a general salve, and there\u2019s always scope for hallucination with today\u2019s AI technology, so you always have somebody that has to do a bit of a tire kick on what\u2019s being produced,\u201d said Andy Nightingale, vice president of product management and marketing at Arteris<\/a>. \u201cAs things progress, the need for that becomes less and less, because the people who are building the AI technology are teaching it how to work for longer without hallucinating, or at least to double-check itself. That\u2019s not the case today, but it certainly will be tomorrow. The amount of engineering expertise can be reduced, but there still needs to be that sanity check in the loop somewhere. It may be that for the person who specified the functionality in the first instance, it\u2019s enough for them to say, \u2018Is this thing \u2013\u2013 I don\u2019t know how it does it \u2013\u2013 but is it actually doing what I expect it to do?\u2019 You might have a mathematician who knows what the thing is supposed to be doing, and they don\u2019t necessarily know how to code up the FPGA, for example. But they\u2019ll know that the results they\u2019ve been given are correct or not.\u201d<\/p>\n Conclusion Related Articles The Limits Of AI\u2019s Role In EDA Tools<\/a> How AI Will Impact Chip Design And Designers<\/a>
As AI picks up steam, exactly which tasks electrical engineers will do is unclear today. The loss of some jobs along the way is inevitable. But given the industry\u2019s worsening talent shortage, it all may shake out in the end.<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/au\/wp-content\/uploads\/2026\/01\/Screenshot-2026-01-28-at-11.47.29-AM.png\")
Fig. 1: AI-driven chip design inflection point. Source<\/a>: Cadence<\/p>\n
\nJob market shifts: Some entry-level positions may be redefined or merged as organizations adopt AI-powered tools that can accomplish the basics more efficiently.
\nEvolving skill requirements: Universities and training programs are adapting curricula to include AI literacy, tool proficiency, and higher-level problem-solving skills. Graduates are increasingly expected to know how to leverage AI tools to enhance productivity and focus on more complex, strategic work.
\nHigher-level entry points: As AI tools handle basic tasks, new graduates may be able to start at a higher level, working on more advanced projects sooner than before. The focus shifts from manual execution to oversight, tool management, and creative problem-solving.
\nHuman skills remain vital: Skills such as critical thinking, collaboration, innovation, and domain-specific expertise are not easily automated and will continue to be in demand.<\/p>\n
As EDA evolves, natural language AI agents and mixture of experts (MoE) machine learning architectures can be trained on a company\u2019s data to help senior engineers work more efficiently, and to move new recruits up the ladder faster by serving as a teaching aide.<\/p>\n
If AI can enable engineers to spend more time on creative problem-solving, it almost certainly will improve overall job satisfaction and morale. \u201cThey\u2019re seeing this already in the software industry,\u201d said Cadence\u2019s Graham. \u201cIt will no doubt trickle into verification and design, and so on. It was simply about, \u2018I\u2019m happier, I feel more in the flow, I feel less interrupted by minutiae and repetitive tasks, and I\u2019m able to focus more engineering energy on creative problem solving, and the areas where I can truly give value.\u2019 This is where the human in the loop will absolutely provide the value.\u201d<\/p>\n
Even With AI Inroads, Human Chip Designers Still Essential<\/a>
Engineers are still needed at key points throughout the design pipeline.<\/p>\n
AI is a set of algorithms capable of solving problems. But how relevant are they to the tasks that EDA performs?<\/p>\n
How AI is reshaping EDA, and how it will help chipmakers to focus on domain-specific solutions.<\/p>\n