Higher Education Can’t Wait for the Future to Arrive (Lev Gonick, Arizona State University)
“The biggest risk we face as a sector is assuming we can wait out AI.”
Everyone in higher ed is trying to figure out what AI means for the university.
Some are excited. Some are defensive. Many are waiting to see what happens next.
Lev Gonick is not waiting. As Chief Information Officer at Arizona State University, he sits inside one of the largest and most closely watched public universities in the country, serving close to 200,000 students annually. ASU has become an important test case for a very different institutional posture: move early, learn fast, and treat AI as a reason to redesign the university around students rather than asking students to keep navigating inherited systems.
That posture makes this conversation especially useful right now.
What I appreciated about Lev is that he does not frame AI as a layer of productivity software on top of the existing university. He sees it as a reason to rethink the university itself: how it is organized, how students move through it, what kinds of support they receive, what credentials mean, and how much friction we have come to accept as normal.
His argument demands agency and vision from university leaders. Universities have lasted for centuries because they are durable institutions. But durability can become a liability when leaders treat it as proof that reinvention can wait. Lev’s view is that higher education has to choose a more active posture. It has to decide what kind of future it wants, then start designing for it.
This conversation also gets at one of my favorite themes: the future of education will depend as much on institutional imagination as on technology. AI creates new possibilities, but universities still have to decide what they are for, who they serve, and what promises they are willing to keep.
-Allison
On Why Higher Education Can’t Wait for the Future to Arrive
ALLISON: What’s a belief you hold about higher ed that most would disagree with?
LEV: There are two narratives that I hear almost everywhere I go.
One, a scarcity mindset has grown almost unchecked across higher education. There is so much hand-wringing about how technology is changing the university for the worse; I keep hearing that we are in a tough position, and it’s only going to get tougher.
Two, many folks both in and out of higher education assume the university is passively waiting for the AI future to arrive. To be sure, many of my colleagues are doing exactly that. It’s been a tough lesson over the last forty years: early adopters may get their fingers singed, and that’s demotivating.
Neither mindset is true at ASU. Maybe it’s in the water, but we work hard to maintain a growth mindset and to build the future we’d like to see realized. We are designing for that future, engaging early, and iterating intentionally and ethically along the way. We call it “Principled Innovation”—everything we do at ASU is grounded in that framework. And I think this approach will help us come out stronger than before.
ALLISON: That relates to my next question, about the remarkable durability of the university. I’ve long believed that the slow pace of change in institutions is a feature, not a bug. Is this still true at a moment when the university’s relevance is being sharply called into question? What does history tell us about what is likely to change in higher education, and what may not?
LEV: Universities have stood the test of time, despite recurring moments of questioning and backlash throughout the centuries. Today’s university system belongs to the legacy of the Greek academies that eventually gave rise to the land grant and research universities—at ASU, we see ourselves as part of the fifth wave of that tradition. For us, that means being of service at a national and global level, helping solve the problems we all face together.
Knowledge creation is the enduring piece of that tradition, and I think it will continue. We will likely do that work differently now, given the massive amount of private sector R&D activity across industries—though it’s worth noting that such activity is guided by commercial principles. Basic science remains hugely important for innovation. Almost all innovation at the front end of the humanities takes place in universities. That will remain critical to universities moving forward.
But we would be wrong to assume a singular institutional representation of what higher education has been, is, and will be. How universities serve different kinds of students will continue to evolve, and their structures alongside it—there will be great universities in the liberal arts tradition, an evolution of public institutions like ASU, and the community college ecosystem will play a critical role in how people navigate changes in our economy. The elite institutions will continue to be places where knowledge creation, scientific discovery, and the arts are centered. I don’t mean to be idealistic—I just think the prospect that universities will land on the trash heap of history is wildly overstated.
The biggest risk we face as a sector is assuming we can wait out AI, because we are irreplaceable as the most important institution in public life. That elite, frankly arrogant, orientation has not served us well. The attitude shift is long overdue, and so too is model innovation. For universities like ASU, the future lies not only in diversifying what we do, but in completely reconceptualizing and redesigning how we serve our community, state, and nation. That’s an existential conversation about what it means to sustain the enterprise called the university.
ALLISON: I’ve been trying to be more pointed about why this wave is different from those prior, especially in how it helps us rethink the project of school and schooling. For those in the wait-and-see camp, there isn’t a clear call to action for starting now. My thesis is that AI’s disruption lies in its ability to replace services with software. Historically, software has enabled faster or cheaper service delivery, but has never replaced the entire service experience; it hasn’t had anything resembling a human interface.
AI is different—that’s why it threatens jobs in ways prior waves have not. For an industry like education, built almost entirely on service delivery, the technology allows us to rethink as much of the enterprise as we want to. That’s the opportunity you’re describing; that’s why universities need to act now.
LEV: I think that’s right. It’s also a rare opportunity. Every once in a while, we have the chance to take a massive step forward. When the histories of the university are written fifty years from now, I think we will see this as a moment when the wheat was separated from the chaff: those who leaned in will have evolved and endured.
On Engineering the University for Continued Relevancy
ALLISON: What does relevancy look like for higher education over the next ten years, as you re-engineer learning around AI?
LEV: The expectations for great universities of the future will be that they continually raise what it means to realize the greatest aspirations of human ingenuity. From scientific discovery to innovation across the arts, we will either shorten the time to new discoveries, and raise the bar on what can get produced among our students and faculty in their careers. That’s right in front of us. And it’s a fantastic time in history to be a researcher, a student, a lifelong learner.
There’s also never been a better time to be participating in educational redesign; I think we are going to develop a strong set of new credentials going forward, and the time for those credentials will shift radically too.
ALLISON: What two or three shifts will ASU make during this time to enable this vision?
LEV: That’s a great question—and I’m going to bring it down to terra firma.
We need to address the built-in inertia that is one of the enduring qualities of the university. We’ve evolved a large bureaucracy organized around discrete functions, and built talent around those functions. Learners have to navigate that bureaucracy on their own. It’s deeply encrusted in how the university is experienced, and that has to change.
We have an opportunity right now to reorient the university around student experience—not as an aspiration, but as a necessity. I’m calling this shift TechEd, which I explore in detail in my LinkedIn series The TechEd Revolution.
AI poses a fundamental shift in how technology might empower students to own their discovery and educational journey, and to drastically reduce the friction that makes college so unappealing to so many.
To that end, we need to urgently redesign systems and opportunities around skills and competencies. That work should be far more advanced than it currently is. And one of the hardest challenges is rethinking how we operate as a workforce in academia. ASU employs 35,000 people, all deeply embedded within that functional orientation—we will have to rethink what roles a student-centered university requires, and how machines as co-workers might fulfill some of that work.
That all requires zeroing in on what we want the learner experience to look like. This vision dispenses with the sage-on-a-stage and envisions instead a model where students take agency over their learning journey, supported, coached, and provoked by people with deep experience along the way.
Finally, universities feel entitled to public trust—but that entitlement is tone deaf to public opinion. We are in a moment where we need to actively regain trust in this remarkable human creation called the university. We will only do so if we truly deliver on our promises to students.
On the University as Data-Rich, and Intelligence-Poor
ALLISON: You’ve said in past conversations that the last few chapters of edtech have failed to enable systemic change—did technological or cultural limitations play a part? Why have the last two eras of ed tech been so underwhelming in your view?
LEV: I think it’s true to say that the first iterations of edtech have been underwhelming, as you say. But that doesn’t mean that the industry isn’t on a transformative trajectory.
When motor vehicles were first conceptualized, they were just a motor attached to a tractor as a farm implement; it took fifty years to arrive at the very first Mercedes-Benz in 1886. Even the ability to travel, and not just work with, motor vehicles had to be imagined and designed for. Today, in markets like Phoenix, we have autonomous vehicles everywhere—a very different traveling experience than what came before. You’re still in a vehicle with four wheels, no user-facing controls; the configuration is entirely different. That’s another 140 years from first iteration to transformative innovation.
Edtech’s earliest ambition was to simply digitize existing campus functions. It’s taken us 30 years to realize that technology can radically change the educational experience by reducing friction within the student journey and unlocking the art of the possible for learners.
It’s also worth noting that we have been data-rich for quite some time, but have historically used that data to support the university’s functioning rather than the student experience. We are now positioned to leverage that incremental progress into something closer to a step-change, if we can overcome institutional inertia.
ALLISON: I find it remarkable that we can tell what a student ordered at the cafeteria last week, and yet there is no system that tells me very much about what she knows, her life, or about her goals and interests. Walk us through how we ended up being data rich and intelligence poor, and how institutions could go about changing it.
LEV: We are data-rich in the sense that we have many tools and pools of data, but they are largely defined by those same functional silos I’ve described—data on academics, admissions, career planning, and student experience all housed in separate containers. The brittle solution right now is to use APIs and other technical tools to stitch together those repositories for insights into the student journey. But that’s not a strong foundation for leveraging AI. My challenge to colleagues is this: the university needs to re-architect its approach to data at the enterprise level.
That’s no small undertaking. But it’s also a great leadership opportunity and a chance to partner productively across the sector. We won’t buy our way to a transformed data architecture, but we can work with third parties to rethink how it’s designed. You’ll need subject matter experts and technologists listening directly to students, hearing their aspirations and experiences, and intentionally designing toward those needs—in a truly student-centered way. And leaders will need the tenacity to hold the course.
We are already seeing progress at ASU. When data becomes intelligence that illustrates and supports students’ success and ownership over their experience, students are supported—not inhibited—by how data flows across campus. That’s a big win.
One Small Signal
ALLISON: What’s one small signal in the world to which we should be paying more attention?
LEV: The conversations that happen around the kitchen table contain the most important signals about human aspiration. Those signals have been drowned out by the cacophony of breathless headlines and dystopian or utopian visions—but they tell us what matters and what we should be building for. What do you want to be when you grow up? What do you want to achieve? What do you want to get good at? What are you scared of? Who do you want to help?
Those answers are the signals we should listen to and align our intentions around.