Google's Head Of Learning Says AI Can't Solve Education's Real Problem

Ben Gomes's mother was a geography teacher in India, but after her career was cut short by an allergy to chalk dust, she taught her children at home instead. She planted something that would shape the next five decades of her son's life: curiosity. Gomes went on to spend 21 years building Google Search, overseeing the system that processes billions of queries a day. Today, as Google's Chief Technologist for Learning and Sustainability, he is one of the company's most senior figures thinking about the future of education and the company's broader push to bring AI into education worldwide.

When I sat down with Gomes, I wanted to understand something specific. In a moment when AI companies are racing to build automated tutors and personalized learning engines, what does one of the key leaders for Google's education efforts actually believe is the most important problem in learning?

His answer was disarmingly simple: motivation. And he doesn't think AI is the solution.

The Why, Not The How

"The technology can improve how you learn and the details of it," Gomes said, "but the why you learn is a very human thing."

It's a striking claim from someone whose employer is investing billions in AI-powered education tools. But Gomes kept returning to this distinction throughout our conversation, that the mechanics of learning are increasingly solvable with technology, while the desire to learn remains stubbornly, beautifully human.

He grounded it in personal observation. "I don't know anybody, who is a high-achieving individual in life, who would say, I was unlocked by a book," he said. "They're almost always unlocked by a person."

He paused. "It's almost always a teacher who said something, who treated them differently, who made them feel differently about the process of learning."

As a former classroom teacher myself, working in post-industrial towns in the north of England, this rings true. I spent as much time on classroom management as I did on actual teaching. The students who thrived weren't the ones with the best resources, they were the ones who had been, as Gomes puts it, "unlocked" by someone who made them feel they mattered.

"Once they felt they mattered in that circumstance, then they could run forward," he said. "And I think that is where all the tools can come in, to help them move forward."

The implication is clear: AI is the accelerator, not the ignition.

From Spelling Correction To The Future Of Learning

What makes Gomes's perspective unusual is the road that brought him to it. On of his big projects working on Google Search was spelling correction. His project partner on that work was Noam Shazeer, who went on to co-author "Attention Is All You Need," the 2017 paper that introduced the transformer architecture underpinning every major AI model today.

"The core problem in search was a problem of language," Gomes said. And he drew a direct line from that early work to what generative AI now makes possible in education.

"One of the biggest use cases for language is actually propagating information from, in some sense, one generation to the next, to build on what's gone before," he said. "We say we stand on the shoulders of giants, but the literal way in which that happens is through the process of language."

When language was finally "unlocked" as a tool by AI, something Gomes admitted he didn't think would happen in his lifetime, the implications for education became enormous. "I didn't think it would actually happen in my lifetime. It looked like AI would never reach that point because it hadn't for so long. 40 years of trying, or more."

Now it has, and Gomes sees education as perhaps the most consequential application. AI has, in effect, cracked the language problem that held back machines for decades. But he's cautious about declaring victory for education. "I think we still are kind of at the beginning," he told me. "It's a complex journey."

The reason for that caution keeps returning to the same place: language can carry knowledge, but it cannot carry desire. AI can now transmit and transform information with extraordinary fluency. What it cannot transmit is the reason a student would want to receive it.

The Renaissance Learner

That distinction between what AI enables and what it can't create, shapes how Gomes thinks about what schools should teach next. He argues that we're reversing decades of hyper-specialization, and that education needs to catch up. But the shift only works for students who already want to learn.

As an example he mentioned web engineers and designers. "People who are engineers can also do some amount of design because there's design tools available," he said. "Designers can also begin to do some amount of coding."

But he was careful to distinguish between mechanics and concepts. "The details of the syntax of a language have become vastly less important. But the concepts of programming still remain important. How do you break a thing down into modules? How do you think about interfaces? How do you think about levels of abstraction?"

This, he argues, should reshape what schools teach. "I think the education system gradually needs to evolve to provide some of this higher-level conceptual understanding of an area, as opposed to more of the mechanics of doing the thing, because the mechanics of doing the thing becomes increasingly automated."

He invoked a historical parallel. "For a long while historically, there's been a narrowing of what somebody learned until they were a very narrow expert in a narrow area," he said. "If you go back, there was a concept of the Renaissance person who knew kind of what there was to know of any value."

He's not predicting a return to that — "the scope of knowledge is way too vast today" — but he does see AI enabling a meaningful broadening from the narrow specializations that have defined professional life for generations.

The crucial part is that broadening is only possible for people who are already moving. A student with no interest in learning doesn't suddenly become a Renaissance learner because the tools got better. The tools amplify direction, they don't provide it.

The 5% Problem

If motivation is education's real problem, then the equity question becomes even more urgent. Because the gap that matters most may not be access to tools. It may be access to the people who create the desire to use them.

Gomes is candid about this risk, a concept he calls "the 5% problem."

The concern is straightforward: if AI-powered learning tools are expensive, or if only well-resourced schools adopt them effectively, the benefits concentrate among those who already have the most. With the cost of agentic AI subscriptions climbing in recent months, this isn't theoretical. But Gomes's framing suggests the problem runs deeper than price. A free tool is still useless if nobody in a student's life has made them want to pick it up.

Gomes outlined Google's approach across multiple levels. "We've made a lot of stuff available for free, including some of our best models," he said. He pointed to Chromebooks as cheap devices for basic access, to Android phones, and to efforts around internet availability.

But he was honest about limits. "None of those things are perfect, and those things are of a scale that is way beyond any single company," he said. "Those are government-level questions of basic infrastructure."

Where he sees Google’s specific contribution is in teacher training. "We're working a lot to create teacher training materials in many different formats and small chunks, so that teachers can consume it on the fly," he said. Google's recent commitment to help train educators across the United States, alongside a $150 million investment in AI education grants, represents its largest push in this direction.

A six-month pilot with Northern Ireland's Education Authority found that teachers using Google's AI tools saved an average of 10 hours per week, time that was redirected toward student engagement and professional development.

But Gomes was clear that success depends on more than distributing tools. "Where I've seen success is where the school districts are really leaned in," he said. "The teachers are talking amongst themselves, they are owning the tools and actually making it their own." He pointed to Miami-Dade County, where he's observed teachers whose "interactions with students becoming more personal, more time with the students in the context of the class."

What Happens When Teachers Start Building

If AI can't provide the spark, what does it look like when the spark is already there? Perhaps the most vivid illustration came from an unexpected place: a vibe coding session.

Google recently hosted the 56 U.S. State Teachers of the Year at its Mountain View campus. As part of the visit, the teachers participated in a session where they used AI to build functioning software.

"It was amazing to see what kind of creativity it unlocked," Gomes said. "For many others, they had not considered that this was an option that's available to them."

One example stood out. "Special ed teachers who were working with kids with disabilities had coded up apps that allowed the student to use their blink reflex — blinking and moving their body — who otherwise had other significant disabilities, to create music."

Gomes emphasized the significance: "This is not software that exists. It's something that they saw the need for. They created that for the particular students that they had."

Teachers using AI to build things that no product team would ever think to create, excites him most. And it proves his central point: the special ed teacher didn't build that app because AI suggested it. She built it because she cared about a specific child. The motivation was hers. The AI was just the tool that made it possible.

At a conference last week, an educator whispered to me that they spend their evenings and weekends building AI agents and spinning up their own software, but don't feel they can talk about it at school. They feel alone, surrounded by colleagues who "wouldn't get it." That gap between the teachers who are already building and those who haven't started, may be the most urgent divide in education right now.

Gomes sees this as a fundamentally human problem, not a technology problem. "What I see is these communities of practice — the teachers who are at the leading edge helping others come along — those kinds of things actually play a pretty big role," he said.

The Attrition Argument

There is, of course, a fear that hovers over every conversation about AI and teaching: that time saved will simply be filled with more work. Teachers have seen this pattern before when a colleague leaves, the role isn't replaced, and the workload gets divided among those who remain. Could AI just accelerate that cycle?

The stakes are directly connected to Gomes's central argument. If the real problem in education is human motivation and if that motivation is sparked by teachers, not tools, then losing teachers is the worst possible outcome. Every teacher who burns out is one fewer person who might have "unlocked" a student.

Gomes reframes it through the lens of retention. "There's a projected worldwide shortage of something like 44 million teachers worldwide," he said. "There's a huge amount of need to make the profession be what many teachers come into it hoping it will be, which is their ability to really shape the lives of young kids and young adults. But when they get to it, so much of it is not that."

Google’s Northern Ireland study showed teachers saving enough time to reclaim their weekends. For Gomes, that isn't an efficiency story, it's a survival story for the profession.

"If you have a profession where you have to work late every day and on weekends, it is not surprising that you end up with burnout and attrition," he said. His hope is that as administrators see reduced turnover, they'll recognize the value of letting teachers use recovered time for what they entered the profession to do and for the thing that AI, by Gomes's own admission, cannot do, that is building the relationships that make students want to learn.

The Chalk Dust And The Algorithm

Ben Gomes's mother couldn't stay in teaching because of chalk dust. Today's teachers are leaving for different reasons — burnout, administrative burden, a sense that the job has become something other than what they signed up for. The barriers are different but the loss is the same.

The technology Gomes spent a career building can remove some of those barriers. It can save time, translate languages in real time, turn curricula into podcasts, and let a special education teacher build a music app for a student who communicates through blinks.

What it cannot do is make a student feel like they matter. That, Gomes insists, remains the work of people.

"All of this has to work through that primary relationship of why do you want to learn," he said, “which is where I think the teachers come in. Where my mother came in.”