Shishir Mehrotra, recognized on this year’s A.I. Power Index, has positioned Grammarly as what he calls an “A.I. superhighway” that spans over 500,000 applications. As CEO, Mehrotra is pursuing an ambitious goal: making Grammarly one of the top three or four A.I. companies globally. His strategy diverges sharply from tech giants like Microsoft and Google, rejecting their walled garden approach in favor of meeting users wherever they already work—whether in Gmail, Slack, LinkedIn or countless other platforms. Drawing from his experience helping scale YouTube, Mehrotra believes the most powerful platforms don’t try to own everything but rather make everything else more valuable. With over 40 million users and partnerships across over 3,000 educational institutions, Mehrotra envisions a fundamental shift in workplace productivity where every employee manages teams of A.I. agents rather than simply using A.I. tools, transforming humans from individual contributors into conductors of A.I. networks capable of achieving exponential productivity gains.
Your goal is for Grammarly to become one of the world’s top A.I. companies. How does your platform strategy of bringing A.I. agents to users wherever they work create sustainable competitive advantages against ecosystem players like Microsoft and Google? Where do you see the biggest opportunities for disruption?
Everyone assumes we’re David going up against Goliath, but we’re playing a completely different game. Microsoft and Google build walled gardens; they want you living entirely in their ecosystem. We meet users everywhere they work. That’s what I mean when I call Grammarly an A.I. superhighway. We’ve spent 16 years building infrastructure across 500,000+ applications. When you’re writing in Gmail, a message in Slack, or a post on LinkedIn, we’re right there with you, not trying to pull you somewhere else, just making you better at what you’re already doing.
For years, we’ve only run one car on this superhighway, your high school English teacher. But what if we ran dozens of agents? When you’re writing that customer email, you’ve got your grammar agent, plus your sales agent pulling customer data, your product agent filling in feature details, your support agent flagging recent issues. Multiple agents working together where you already work. This is the direction we’re headed.
Instead of asking, “How do we get users to switch to our ecosystem?” we ask, “How do we make every ecosystem better?” I learned at YouTube that the most powerful platforms don’t try to own everything; they make everything else more valuable. That’s exactly what we’re building.
Your A.I. agents can now predict grades, detect A.I.-generated content and provide sophisticated writing assistance—yet only 18 percent of students feel “very prepared” to use A.I. professionally after graduation, while two-thirds of employers plan to hire talent with specific A.I. skills. How are you navigating the balance between empowering users with powerful A.I. capabilities while ensuring they develop the critical thinking and communication skills that will make them valuable in an A.I.-augmented workforce?
This is a big tech shift that’s faster and certainly more disruptive than previous ones, and I know it’s a challenging position for both educators and students to be in. They’re being forced to adapt in ways they haven’t had to in a long, long time. It reminds me of when calculators were first introduced in the classroom. Educators feared that math skills would erode, a concern that now mirrors worries about A.I. undermining critical thinking. The key is teaching students when to use A.I. tools, when to limit them and how to reimagine meaningful assessments.
We built our new agents to teach responsible A.I. partnership rather than replacing student thinking. For example, when our Citation Finder agent flags an unsupported claim, students learn why evidence matters and how to evaluate credibility while the agent guides them to quality sources. Our AI Grader agent provides rubric-aligned feedback before submission, helping students develop self-assessment skills and improve their work—that’s a great tool I wish I had when I was in school.
This hands-on experience with specialized agents builds both writing skills and A.I. literacy, which is crucial since these are exactly the skills students will need when they enter the workforce. What I find particularly validating is what we’re seeing in the field. Grammarly partners with over 3,000 educational institutions, and educators consistently tell us that employers are actively seeking graduates who can use A.I. effectively. Some employers are even coming directly to universities asking to identify students skilled with A.I. because they want to hire them. This validates our focus on building genuine competency rather than dependency. With Grammarly, students maintain control of their work and original voice while learning to be strategic partners with A.I. We see this as a moral imperative: preparing today’s students for a workplace that already depends on these tools while ensuring they develop the critical thinking and communication skills that will make them valuable in the A.I.-powered workplace.
Your research shows that 66 percent of professionals expect a 3x productivity increase within five years, with industry leaders predicting 10x gains. As someone who helped scale YouTube to become the world’s largest video platform and now leads a company processing 25 trillion tokens annually, what specific productivity breakthroughs do you believe will drive these exponential gains, and how will the relationship between humans and A.I. agents need to evolve to realize this potential?
My view is that there are three phases of productivity. The first was digitization, typewriters to word processors. The second was knowledge-work collaboration, how to work with people on applications like Google Docs, Office and Slack. Now we’re entering the third phase: working with A.I. agents. But here’s what’s different about this phase: it’s not just incremental improvement. It’s architectural. When YouTube went from handling thousands of videos to billions, we didn’t just make the servers faster, we rebuilt how the entire system worked.
Here’s what I see coming: imagine showing up to your first job and already having a team of 50 direct reports. They’re not people, they’re A.I. agents. They work 24/7, never argue and know everything in your knowledge base. The skills that humans are going to need more of are management. Every employee will start off managing a team of agents. That’s the new baseline.
The relationship between humans and A.I. has to evolve from tool usage to team leadership. We’ll stop being individual contributors who use A.I. tools and become conductors of A.I. networks. The 10x productivity gains aren’t about humans working faster, they’re about humans learning to orchestrate A.I. systems so effectively that we amplify our capabilities exponentially. And based on what I’m seeing in the market, that transition is happening faster than most people realize.
What’s one assumption about A.I. that you think is dead wrong?
The biggest assumption that people get wrong is that A.I. is going to completely replace human jobs. We’ve seen technology get better and better over the past few decades, and in every case, we’ve seen fears about how it might change the way people are situated in the workforce. Technological advances have ultimately empowered humans to be more productive rather than replace them. The jobs of the future won’t be “human does X, A.I. does Y.” They’ll be “human conducts an orchestra of A.I. agents to accomplish goals that neither could achieve alone. That’s not replacement, that’s amplification at a scale we’ve never seen before.
If you had to pick one moment in the last year when you thought “Oh shit, this changes everything” about A.I., what was it?
When I was visiting our “A.I. superhighway” team in Berlin, I realized how capable and complex our infrastructure is to bring A.I. everywhere users work. Looking at this through an “eigenquestions” lens, the breakthrough wasn’t about a specific A.I. feature, it was reframing the entire problem. Instead of “How do we make A.I. smarter?” the eigenquestion is: “Will A.I. transformation happen through adoption or integration?”
Most A.I. products expect users to change their workflow—new chat windows, new tools, new processes. That’s adoption, and it hits a ceiling. But the real unlock is integration: A.I. that works seamlessly within existing behaviors. That’s the moment I realized that we accidentally solved the hardest part. We’re already embedded in millions of workflows. While everyone builds standalone A.I. that requires behavior change, we can enhance the work people are already doing, where they’re already working.
What’s something about A.I. development that keeps you up at night that most people aren’t talking about?
Business models—so many of the past two decades have been spent on business models that maximize “eyeball time.” It’s what we focused on at YouTube, and also how businesses like Google, Instagram, Facebook, etc., got built. But in an A.I. world, we’re seeing agents take on more and more responsibility and making many tasks need much less “eyeball time.” I think this leans heavily towards subscription business models, but the process of matrixing many agents into subscriptions is quite complex. People close to me know that I am quite enthusiastic about bundling. Perhaps that’s the answer?
