Workflows and the addition of new capabilities are happening much faster than with previous technologies, and new grads may be vital in that transition.
Key Takeaways:
AI is expected to eliminate many repetitive, entry-level tasks, but that may allow engineering students trained on the latest tools to start in more senior positions. AI is a force multiplier. It can accelerate the learning curve for junior engineers. While AI is very good at solving multi-dimensional problems, domain expertise, critical thinking, and sanity checks will remain essential.
AI is almost certain to eliminate many entry-level jobs in chip design by automating repetitive and data-intensive tasks, but there is a corresponding expectation that today’s engineering students will be trained using these tools so they can enter the workforce higher up the ladder.
Many engineers liken the current era to the Industrial Revolution, which replaced hand tools, or the advent of automobiles replacing horses. An ongoing talent shortage requires more efficient use of engineers, and AI can help. But it’s unclear how widespread or deep the disruptions will be.
There are two schools of thought about its impact. “One angle is, I have an established workflow, and I need people who can ask, ‘What in this workflow could be enhanced and/or replaced by an AI?’” said Alexander Petr, senior director at Keysight EDA. “Another group of people needs to say, ‘What if we throw out the whole workflow and retool the whole thing?’ Both have merits. Wherever you go, everything you look at has a certain amount of culture and meaning. People are so accustomed to doing things a certain way that it’s hard to break out. That explains why you have this group that says, ‘Let’s use AI to enhance,’ and you get questions like, ‘Can AI substitute for four people I don’t have?’ Basically, the AI is asked to do the same job as the engineers. The AI is asked to think the same way as the engineers, and it’s asked to create the same output as those engineers. That makes it much harder to achieve than potentially going with the second group, which says, ‘What if I don’t do it the same way as the engineers do? What if I try to re-engineer the problem and I use the AI to the point where it’s more capable of looking at a high-dimensional problem beyond what humans are able to do? And what if I take the next step in automation and use AI to automate it?’”
Others point to two types of seniority, with one more easily replaced than the other. “One 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,” observed Kexun Zhang, head of research at ChipAgents. “The 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’ve 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’t really need to know that much detail about the lower level of abstraction. That is true for EE. That is true for CS.”
Existing tools at a lower level of abstraction may not be needed for an engineer’s education, but there is still value in becoming proficient on those tools. “Of course, we still need people to know all these different levels of abstraction, but we don’t need that many junior engineers to go deep into the abstraction,” Zhang said. “They 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.”
This solves the problem of how engineers gain expertise if AI takes many of today’s junior jobs. “This is a topic of conversation with me and my friends, and basically our whole company about recent grads,” said Daniel Rose, founding AI engineer at ChipAgents. “There are a lot of people who have been PhD, Master’s, 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.”
In fact, mid-level engineers may find the AI-driven job shift the hardest. “Entry-level engineers will be very used to using AI tools, and they are on the learning curve where they understand aspects of it,” said Nandan Nayampally, chief commercial officer at Baya Systems. “There 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’s finally a tool. How you use it best is up to you.”
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