Kathleen Kramer, a professor of electrical engineering at the University of San Diego, has already begun her term as the 2025 president of the Institute of Electrical and Electronics Engineers (IEEE).
IEEE calls itself “the world’s largest technical and professional organization dedicated to emerging tech,” and IT Brew caught up with Kramer at the org’s booth at CES to talk about the org’s recent Impact of Technology in 2025 report. That survey found 58% of respondents think AI will be the most important technology this year—but also that mass enterprise adoption isn’t quite around the corner, as around one in five reported their orgs are rethinking their strategy or only beginning to explore it.
This interview has been lightly edited for content and clarity.
What did you see as the most important finding in IEEE’s recent tech trends report?
AI was all everybody could talk about last year. This year, 2025, is supposed to be the year of quantum [computing]—but I think we’re still waiting for real stuff, real activity on quantum. Whereas AI, you have genuine products, you have genuine activity.
From the point of view of the IEEE president, AI has helped converge and enhance many things that were kind of dying on the vine, because you can’t succeed at them with AI unless you have these things. Up until, say, two or three years ago, it was all software, software, software, software, software—and so all the hardware types, you had to be super special or delightful, or you weren’t getting any time. If you talked to who’s hiring, they were, “Hi, we’re hiring software.”
To really do interesting things, you have very complex algorithms, and you need new hardware. And so that’s for my people.
Do you think the industry is getting a little ahead of itself on AI in some way?
I think people can’t help themselves—but they want to be on the bandwagon, don’t they. It’s difficult for people to say, “Oh, here I am, and I don’t really know what I’m doing, and I find this too hard for me.” No one answers that.
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I think that the young—meaning like my typical student, your 20-year-old—are looking for quick, easy things, and they have developed some really bad learning habits. Covid made it much worse, and they haven’t learned how to be persistent—they learn that you’re supposed to come up with an answer very quickly. There’s an engineering lesson that people learn, which is that you expect to learn how to test things and you expect things to fail, and you expect to have to redesign and fix it.
Our twenty-somethings, they didn’t learn that lesson very well. I saw it even before AI, it was software libraries. “Oh, I shouldn’t have to code. I should be able to find somebody else’s coding and use it.” And that’s how they see AI.
What do you see as the areas where AI is closest to maturity?
In terms of autonomy, things have gone at a lightning pace, relatively. It’s caused a cultural shift in a lot of places. You know how aviation (that’s where my background is) has long taken the view that everything you do is risky, and we have to measure the risk, and lives are at stake.
And with cars, people have taken the view that car accidents happen regulary—and so oh, let’s just try it! And then car accidents might happen. On the aero side, even though you know you can’t get past certain points without using AI, the non-predictability of it is too much to bear as a culture. It’s an ethos. It’s the way they see the world to move slowly, whereas quite obviously, multiple cities have cars driving around by themselves, and in the long run that will actually be safer.
Where people encounter it daily is kind of in auto response to searching, and so they might not even realize that they’re getting an AI response. But it is doing a pretty decent job. Like a better job, I would say, than a college freshman.