Why Top Growth Leaders Can't Agree If AI Will Kill Their Jobs

Ask any growth leader about AI and you'll get one of two answers: either it's the greatest accelerant in history, or it's about to make their entire function obsolete. Both camps have compelling evidence. Both are watching the same technology reshape the same industry. And both are right.

The paradox is this: AI has enabled companies like Anthropic to grow from $1 billion to $19 billion ARR in 14 months and Lovable to hit $200 million ARR in under a year with fewer than 100 people. At the same time, HubSpot's VP of Growth is publicly questioning whether dedicated growth teams will exist in five years. The technology creating unprecedented growth velocity is also automating away the work that growth teams were built to do.

This isn't a think piece about the future. This is what's happening right now inside the companies defining how growth works in the AI era.

The Case for Extinction: Growth Teams Are Becoming Redundant

Kieran Flanagan doesn't mince words. As someone running growth and AI implementation at HubSpot, he's watched AI collapse the traditional boundaries that justified separate growth teams in the first place.

Growth teams are primarily focused on how do we make the go-to-market more touchless? How do we help onboard people? How do we get people to use the product? And what I think AI does is extrapolate all that away to just like, how do we make our go-to-market much more AI-centric?

— Kieran Flanagan, CMO (formerly VP Growth) at HubSpot

His argument is structural, not speculative. Growth teams exist because companies needed specialists to optimize the gap between acquisition and activation, between free and paid, between touchless and human-assisted. But when a multimodal AI agent can onboard users, answer questions, guide product tours, and handle upgrade conversations, who owns that experience? Growth? Sales? Customer success?

Flanagan's answer: an AI innovation pod that thinks across the entire go-to-market, not just the self-serve funnel. At HubSpot, they're already building toward this. They have people working on AI chat and multimodal components that serve both the product-led and sales-led motions. The old division—growth optimizes touchless, sales enables humans—doesn't map to a world where AI does both.

The work growth teams spent years perfecting is being automated faster than most people realize. Tooltips, product tours, email nurture sequences, A/B testing frameworks—all of it is being replaced by AI agents that can personalize onboarding in real-time, diagnose friction points, and adapt messaging on the fly. Flanagan's team at HubSpot launched an initiative called CASH (Cloud Accelerates Sustainable Hypergrowth) specifically to automate growth experimentation using Claude, Anthropic's AI model. It's already delivering results.

The Case for Acceleration: AI Created the Fastest Growth Runs in History

Elena Verna tells a different story. As head of growth at Lovable, she's living through what might be the fastest ramp to $200 million ARR ever recorded. They launched in November 2024. Hit $100 million ARR in roughly seven months. Doubled to $200 million just four months later. They did this with fewer than 100 people.

I feel like only 30 to 40% of what I've learned in the last 15 to 20 years of being in growth transfers here because we just need to invest in such bigger bets and innovate and create new growth loops here. I usually spend maybe 5% innovating on growth in my previous roles. Right now I'm spending 95% innovating on growth and only 5% on optimization.

— Elena Verna, Head of Growth at Lovable

For Verna, AI didn't kill the growth function. It reinvented it. The old playbook—optimize conversion rates, reduce friction, improve activation metrics—still matters, but it's no longer the primary lever. When your product is an AI coding tool and everyone else is launching competing AI coding tools, optimization won't save you. You need new loops, new distribution engines, new ways to compound growth.

Lovable's strategy centers on building in public, giving the product away to create evangelists, and removing every barrier to trial. When a user asks for free credits to run a hackathon at their company, Lovable says yes. The growth team's job isn't to gate access—it's to engineer word-of-mouth at scale. Verna calls it "blowing their socks off." Ship things people want to talk about. Make the product so good that users become your distribution channel.

Amol Avasare at Anthropic sees the same dynamic. He leads growth for a company that went from $1 billion to $19 billion ARR in 14 months, adding more revenue every few months than companies like Palantir generate in a year. The growth team's mandate isn't incremental optimization. It's building entirely new systems to handle exponential scale.

Leading growth inside of Anthropic is the hardest job I've had in my life. You come into Anthropic, you need to understand that 50, 60, 70% of how you operate in the past, just throw it out the door.

— Amol Avasare, Head of Growth at Anthropic

One example: importing memory from ChatGPT. Activation is a massive challenge in AI products because users don't know what to do with a blank canvas. Anthropic let users bring their conversation history from ChatGPT into Claude, giving the AI instant context and users instant value. It was a growth lever that didn't exist in the pre-AI world.

The Real Divide: Optimization Versus Innovation

The split isn't really about whether AI helps or hurts growth teams. It's about what growth teams were doing before AI arrived. If your job was optimizing email subject lines, improving paywall placement, or running multivariate tests on signup forms, AI is eating your lunch. Those tasks are now automatable.

If your job was inventing new growth loops, identifying untapped distribution channels, or building systems that compound over time, AI just handed you a superpower. The difference is stark. Verna spends 95% of her time innovating. Flanagan's team is automating the other 95%.

Cem Kansu, CPO at Duolingo, puts it plainly: consumer products live and die in the pixels. Duolingo has always been a product-led growth machine, but AI hasn't replaced the product team. It's made the product better. They used GPT-3 to build conversation features that teach speaking in ways traditional software never could. The growth insight wasn't "let's A/B test our paywall again." It was "let's use LLMs to solve a pedagogical problem users actually have."

Nicolas Rojas, founder of DAPTA, a no-code AI agent platform, built his company on this thesis. He watched traditional software agencies commoditize and compete on price. His answer was to build tools that let non-technical users create AI agents for sales and go-to-market use cases. The growth motion isn't about optimizing funnels. It's about enabling users to automate their own growth.

Esta vez no quería ser el que implementaba, el que daba consultoría. Esta vez yo quería ser el que hacía una muy buena herramienta, que después le pagabas a alguien más para que te hiciera una consultoría para cómo usar la herramienta.

— Nicolas Rojas, Founder at DAPTA

What Stays, What Goes, What Nobody's Solved Yet

Even the optimists agree: parts of the growth playbook are dead. Verna says only 30 to 40% of her prior experience applies. Avasare says you need to throw out 50 to 70% of how you used to operate. Flanagan is watching entire job functions collapse into AI-driven workflows.

What stays? Distribution instincts. The ability to identify where users congregate and how to reach them. At Lovable, that's building in public on social, founder-led content, and employee advocacy. At Anthropic, it's solving activation problems unique to AI products. At Duolingo, it's pixel-perfect design that turns education into a game people can't put down.

What goes? Repetitive optimization work. Email drip campaigns that could be personalized by AI. Onboarding flows that could be handled by a multimodal agent. A/B tests that AI can run and analyze faster than humans ever could. Fernando del Rio, Chief Growth and Marketing Officer at Talysis, saw this firsthand scaling marketplaces in Latin America. He went from building inside sales teams at Mercado Libre to automating customer acquisition at Linio using WhatsApp, Salesforce, and Einstein AI. The playbook shifted from "hire more salespeople" to "build systems that scale without headcount."

Empezamos a integrar un poco con inteligencia artificial de Einstein de Salesforce para ver cómo los equipos comerciales podían hablar y de ahí pues me roba Linio.

— Fernando del Rio, Chief Growth & Marketing Officer at Talisis

What nobody's solved yet? Product-market fit in AI is not static. Verna says every company has to recapture it every three months because the technology evolves that fast. Competitors launch. Models improve. User expectations shift. Growth teams that treat AI products like SaaS products are already behind.

The Talent Question: Who Survives This?

If the growth function is splitting into those who automate and those who innovate, the skills gap is real. Avasare got his job at Anthropic by cold-emailing Mike Krieger with a pitch so good Krieger responded immediately. He's now the only PM Krieger has ever hired from cold email. That's a signal. The bar for talent just went up.

Flanagan's vision of AI innovation pods requires people who can think across sales, customer success, product, and growth. Generalists who understand where AI fits and where humans still win. Verna's team at Lovable needs people who can invent new loops, not just optimize old ones. Kansu at Duolingo still believes the product manager is not a mini-CEO—they're someone who understands what users will respond to when they see it in the app.

Jeremy Goillot, former head of growth at Spendesk, who now runs The Mobile-First Co., frames it differently. He raised $12 million in seed funding by proving he could build global products with almost no resources. His advice, borrowed from Andreessen Horowitz: if you don't have an idea, join a top company with massive growth and learn how they execute. The implication is clear—if you're going to survive in growth, you need to be at a company where the stakes are high and the learning curve is vertical.

What This Means for You

The growth leaders who see AI as an existential threat are right. Entire categories of growth work are being automated. If your value is executing tactics someone else designed, your job is at risk. The growth leaders who see AI as the ultimate accelerant are also right. The fastest-growing companies in history are growing because of AI, and they still need people who can figure out what to build, where to distribute, and how to turn users into evangelists.

The unifying insight is this: AI didn't kill growth. It killed mediocre growth. It killed growth teams that optimized around incrementalism and called it strategy. It rewarded growth teams that invented new loops, took big bets, and moved faster than their competition could react.

Flanagan might be right that growth teams as we knew them are becoming redundant. Verna might be right that the best growth work of the next decade will look nothing like the last. Both can be true. The question isn't whether AI changes growth. The question is whether you're building the skills to grow in an AI-first world—or optimizing a playbook that's already obsolete.