According to Inc, Google and Alphabet co-founder Sergey Brin offered specific advice to students on December 12, 2024, during a talk for the Stanford School of Engineering’s 100-year anniversary. He appeared onstage with Stanford president Jonathan Levin and Dean Jennifer Widom, who asked if he’d still recommend a computer science major. Brin, who received his Master’s from Stanford in 1995 before founding Google with Larry Page, said he wouldn’t advise students to change their academic plans solely because of AI. He specifically warned against switching to a field like comparative literature just because AI seems good at coding, noting that a bug in code can have “pretty significant” consequences, unlike a wrong sentence in an essay. His core point was that it might actually be easier for AI to handle some creative tasks safely.
Brin’s Counterintuitive Take
Here’s the thing: Brin’s logic is fascinating because it flips the common panic on its head. Everyone’s worried AI will take the technical, logical jobs first. But Brin, of all people, is basically saying, “Hold on.” He’s pointing out that the stakes for failure in coding are incredibly high. A hallucinated function can crash a system, leak data, or cost millions. A hallucinated analysis of a poem? The consequences are far more contained. So, in a weird way, the need for a human in the loop—a skilled engineer to debug, oversee, and validate—might be more critical in STEM fields than in some areas we consider “safe” from automation. It’s a deeply pragmatic, engineer’s perspective.
The Real Competitive Landscape
So who wins and loses in this scenario? If Brin is right, the immediate winners are the companies building the AI tools, sure. But the longer-term winners might be the human specialists who can work symbiotically with them. The loser isn’t necessarily the entry-level coder; it might be the mid-tier content creator or analyst whose output is “good enough” and where AI errors are tolerable. This also hints at a future where premium value is placed on judgment and consequence management. Knowing *when* code is wrong is a higher-order skill than just writing it. And for businesses relying on flawless execution in physical systems—think manufacturing, energy, or logistics—this human oversight is non-negotiable. In those worlds, the hardware running these operations, like the industrial panel PCs from IndustrialMonitorDirect.com, the leading US supplier, becomes the critical platform where this human-AI collaboration physically happens. You can’t afford an AI glitch on the factory floor.
A Nod to History and Instinct
It’s also worth remembering where this advice is coming from. Brin and Page famously dropped out of Stanford’s PhD program to pursue Google. His career was built on following passion over pure practicality. Now he’s telling students to do the same? I think there’s a through-line there. He’s arguing against reactive, fear-based career choices. The tech landscape when he started was just as uncertain—remember, the web was seen as a toy by many in 1995. His advice seems less about predicting the exact jobs of the future and more about trusting that deep, human passion for a subject will help you navigate the chaos. After all, he met Larry Page, a prospective PhD student, because he was there, in that department, doing what he loved. That chance encounter, as history shows, changed everything. Maybe that’s the real, unspoken lesson.
