According to GeekWire, Boston College professor Sam Ransbotham is witnessing a troubling trend in his machine learning classroom where students are using AI to achieve mediocrity rather than excellence. The professor, who also hosts MIT Sloan Management Review’s “Me, Myself and AI” podcast, observes that while some students accomplish amazing things with AI, others are simply “phoning things into the machine.” This creates what he calls “a divide in technology interest” rather than the traditional digital divide, since Boston College provides premium AI tools to all students regardless of socioeconomic status. Ransbotham warns we’re seeing “a race to mediocre” where AI makes it incredibly easy to achieve average results quickly. He contrasts this with Boston College’s motto “Ever to Excel” and worries that the ease of reaching mediocrity hampers students’ path to excellence.
The mediocrity acceleration problem
Here’s the thing that really struck me about Ransbotham’s observations. We’ve spent decades worrying about the digital divide being about access to technology. But now we’re seeing something potentially worse—a divide in how people engage with technology when they have equal access. Some students are using AI as a creative partner, pushing boundaries and learning deeply. Others are treating it like a smarter autocomplete, basically outsourcing their thinking.
And honestly, this isn’t just happening in classrooms. I see it in workplaces too. The temptation to let AI do the heavy lifting is real, especially when deadlines loom. But Ransbotham’s point about “cursory usage getting cursory results” hits home. It’s like the difference between someone who learns to cook by following recipes versus someone who understands flavor profiles and techniques. Both can make dinner, but only one can create something truly exceptional.
business-of-measuring-ai-s-value”>The tricky business of measuring AI’s value
Ransbotham draws this fascinating parallel between today’s AI revolution and Wikipedia’s impact on Encyclopedia Britannica. Remember when everyone thought Wikipedia would destroy knowledge? Instead, it made information more accessible while changing how we measure value. Encyclopedia Britannica had clear economic metrics—books sold, employees paid, printing costs. Wikipedia’s value is harder to quantify but arguably more transformative.
AI faces the same measurement problem. How do you measure the value of slightly better decisions, or time saved on research, or creative sparks that wouldn’t have happened otherwise? Traditional business metrics might not capture what really matters. It’s like trying to measure the value of having a really smart colleague who sometimes says ridiculous things but makes you think differently.
When being wrong is useful
This might be the most counterintuitive insight from Ransbotham. He says AI often gives him “absolute garbage” responses, but that garbage sparks new thinking. The very wrongness pushes him to question why it’s wrong and explore different angles. That’s such a human way to approach technology—using its failures as creative fuel.
Think about it. How many breakthroughs have happened because someone saw something wrong and wondered why? AI’s mistakes might actually be one of its most valuable features for creative and critical thinkers. It’s like having a brainstorming partner who throws out wild, sometimes terrible ideas that somehow lead you to better ones.
Cutting through the AI noise
Between the AI hype machine and the doomsayers, Ransbotham’s podcast Me, Myself and AI aims to find the signal in the noise. And honestly, we need more of that balanced perspective. AI isn’t going to save the world or destroy it—it’s a tool that amplifies our own intentions and capabilities.
As Ransbotham’s work at Boston College shows, the real challenge isn’t the technology itself, but how we choose to use it. Will we settle for AI-assisted mediocrity, or push toward AI-amplified excellence? The answer probably depends on whether we see AI as a shortcut or a collaborator. And that distinction might be what separates the next generation of innovators from everyone else.
