Why Are We Professors Doing “Research As Usual” While AI Systems Advance at Breakneck Speed?

Introduction

Introduction

Academic life traditionally operates at a measured pace — incremental projects, steady funding cycles, and familiar expectations. Yet in the face of AI’s explosive growth, particularly in large language models, we may be clinging to a way of working that risks obsolescence. Why are we continuing “research as usual” when these systems are poised to reshape the very landscape of our disciplines? It’s time to ask whether the future will look anything like the past — and how we, as professors, might adapt before it’s too late.

I am a professor at an elite university. I write papers. Some people read those papers. I apply for grants. I supervise my students and act as if the future will mirror the past. This might seem especially odd because, as co-director of the CIFAR Learning in Machines and Brains program, I have a front-row seat to AI’s rapid progress. Large language models used to be barely better than a standard web search; then they reached undergraduate-level aptitude, then Master’s-level, and soon they may match my top PhD students. As AI accelerates, shouldn’t our research priorities shift to help humanity navigate the coming changes?

I Am Not an AI Doomer

Some believe that AI is bound to destroy what we cherish, while others hail it as humanity’s greatest ally. In truth, I see AI as one of many revolutionary technologies with potential for both good and harm. The question is not whether AI can help us, but how we channel its capabilities to serve broadly shared goals — rather than letting it exacerbate existing inequities. The doom-versus-utopia framing distracts from the real work of guiding AI’s integration into society.

Sure, AI could bring less drudgery, less “busy work,” and more efficient markets. It could even help deliver high-level expertise to populations previously left behind. But why are we academics trying to do the equivalent of perfecting hand looms while the world rushes to build fully mechanized ones.

Photo by Chris Chow on Unsplash

Do Not Work on AI Itself

AI is already advancing at breakneck speed — so swiftly, in fact, that our social, legal, and ethical institutions can barely keep up. If you believe this pace is dangerously outstripping our preparedness, why pour more fuel on the fire? While we could accelerate AI’s capabilities, is that the best use of our academic freedom? Instead, consider directing your talent toward areas like AI policy, safety, and governance, where robust frameworks are urgently needed. Speed alone doesn’t ensure progress; it can just as easily amplify the downsides of poorly understood technology.

Work on Mitigating the Downsides of AI Rollout

The rapid adoption of AI has potential consequences for our democracies, job markets, and governance structures. Academics are well-positioned to study, critique, and propose solutions, such as:

• Democratic Resilience
  The integrity of our elections, public discourse, and trust in institutions could erode if we fail to anticipate AI-driven manipulation. By researching detection methods, implementing transparent oversight measures, and educating citizens, we can help democracy remain robust — even in an era of hyperreal synthetic media.

• Job Market & Economic Stability
  If AI automates large swaths of labor, can we develop frameworks — policy, retraining programs, or social safety nets — to ease disruptions? Thoughtful, forward-looking scholarship here could head off massive social upheaval.

• Alignment and Safety
  As AI systems inch closer to human-level decision-making, how do we ensure they stay aligned with human values? The path to “safe” AI is complex and contested, demanding careful design and oversight.

• Antitrust & Power Concentration
  A small number of AI corporations could acquire excessive monopoly or monopsony power. Strong scholarship in economics and policy can propose ways to prevent such extreme power concentration.

These are fundamental questions that may decide whether AI empowers or undermines society.

Work on Exploiting the Upsides of AI Rollout

Yet focusing exclusively on risks would ignore AI’s capacity to drive transformative improvements across research fields. Consider the potential for AI-accelerated drug discovery, where modeling tools shrink timelines from years to months. Or the promise of AI-driven climate research, illuminating paths to cleaner technologies. The goal is not merely “pro” or “anti” AI — it’s to strategically harness its positive possibilities while staying vigilant about unintended consequences.

Many fields stand to benefit from AI’s unprecedented pattern recognition and generative capabilities:

• Scientific Discovery
  AI can identify novel hypotheses or candidate materials, potentially accelerating breakthroughs that traditional approaches would take years to achieve.

• Education & Personalized Learning
  Beyond standard chatbots, AI-based tutoring can adapt to individual student needs, surpassing the one-size-fits-all paradigm. This might free educators to spend more time mentoring students directly, focusing on conceptual understanding rather than rote tasks.

Work on Ourselves: Preparing for the AI Era

We often assume that a university education equips students for a lifetime. But the AI surge demands new forms of readiness — both for us as faculty and for our students. Being future-ready means updating syllabi to include ethical frameworks for AI usage, rethinking assessments that traditionally rely on rote tasks, and training students to critique AI outputs with rigor and skepticism.

• AI Proficiency
  Learn to wield AI tools effectively for literature reviews, data analysis, or even preliminary hypothesis generation.

• AI Literacy
  Understand AI’s limitations, biases, and failure modes so we don’t blindly trust algorithms or produce flawed analyses.

• Forecasting & Strategy
  Anticipate how AI might reshape your field, which new subfields might emerge, and which longstanding questions might suddenly be solvable — or become obsolete.

We can also re-evaluate the time spent writing grants or incremental papers, redirecting it toward serious engagement with the coming AI revolution.

Work on Reinventing the University for the AI Age

University education has traditionally revolved around carefully structured curricula, with incremental mastery of foundational knowledge. AI challenges that paradigm in profound ways:

• Faster Idea Testing
  Students (and faculty) can leverage large-scale models for immediate feedback or brainstorming, radically speeding up the iterative process of research.

• Personalized Learning Journeys
  Instead of a single syllabus for everyone, students could choose a problem, collaborate with AI systems to tackle it, and pull the needed knowledge on demand.

• Dynamic, Project-Based Approaches
  Faculty become mentors or guides, pointing learners to the right resources — be they textbooks, papers, or online courses — while AI handles routine tasks and scaling.

By reshaping academic structures around deeper problem-solving and student-led inquiry, we can equip the next generation to harness AI in genuinely innovative ways.

Conclusion

Ultimately, we stand at a crossroads: either accelerate the AI arms race or channel our academic freedom into shaping technology that genuinely elevates society. This is not a moment to watch from the sidelines. We must collaborate across disciplines — computer science, law, philosophy, economics, and beyond — to design frameworks that guide AI’s growth responsibly.

By reimagining our research priorities, updating how we teach, and actively steering policy and practice, we can ensure that AI’s sweeping changes serve the greater good rather than undermine it. Speed alone is not our friend unless it’s paired with thoughtful oversight. Academics have a unique kind of autonomy and societal trust. Let’s use that privilege to guide AI’s integration so it benefits humanity — rather than simply advancing a technological arms race.

AI is neither a guaranteed cataclysm nor an unalloyed blessing. It is, however, a disruptive force that demands thoughtful engagement. And the academic community, by virtue of its intellectual freedom, is ideally positioned to shape this transformation for the better. The choice, in many ways, is ours.