The Tools Growth Leaders Actually Use Are Boring (And That's the Point)

The growth industry has a vendor problem. Every week, another platform promises to "10x your acquisition" or "unlock virality at scale." Growth conferences overflow with SaaS booths hawking six-figure contracts for attribution dashboards, personalization engines, and experimentation platforms that require a PhD to configure.

Yet when you ask growth leaders who've actually scaled products to hundreds of millions of users what's in their stack, the answer is almost boring in its simplicity. The most effective growth operators don't rely on exotic tooling. They rely on canonical documents, spreadsheets, and a small set of analytics platforms they actually understand. The counterintuitive truth: the best growth stacks aren't impressive. They're legible.

The canonical document beats the dashboard every time

Naomi Gleit has been at Meta longer than anyone except Mark Zuckerberg. She was employee number 29, ran the early growth team, and has watched the company scale from 30 people to 1.5 trillion in market cap. When asked about her process, she doesn't mention sophisticated tooling. She mentions documentation.

I really believe in frameworks for things. That helps drive extreme clarity. I work on a lot of different projects. A lot of times I'm ramping up mid-project. I'm like, where can I learn what I need to learn about this project? I ask 5 different people, get 5 different answers. That is unacceptable. Of course, I'm sure there's hundreds of docs associated with the project, but there needs to be one canonical doc. Everyone should know exactly where the canonical doc is.

— Naomi Gleit, Head of Product at Meta

This isn't a platitude about documentation hygiene. It's a statement about what actually enables velocity at scale. When Chris Miller helped build HubSpot's early growth team and shift the company toward product-led growth, the team's advantage wasn't their martech stack. It was their willingness to own problems that weren't explicitly assigned to them, triangulate business impact, and move fast. That requires clarity, not tooling.

The actual really small initial growth team, we really had an aggressive mentality, an aggressive approach. We approached the team who owned self-service and we were like, are y'all working on this? And they were like, nah, we're working on a bunch of other stuff. We were like, can we take this? And they were like, sure, if you want it. And so we took it and immediately blew it up.

— Christopher Miller, VP Product, Growth & AI at HubSpot

Miller's point: growth velocity comes from radical accountability and ownership mentality, not from having the right attribution tool. The canonical doc—the single source of truth that everyone can reference—is the infrastructure that makes that ownership possible.

Analytics platforms matter, but only if you actually use them

Laura Schaffer has led growth at Twilio, Rapid, and now Amplitude. She's seen countless companies invest six figures in analytics platforms they barely touch. The pattern is consistent: teams buy sophisticated tools, spend months on implementation, then revert to SQL queries and spreadsheets because the dashboards don't answer the questions they actually have.

When Schaffer joined Amplitude as head of growth, she faced a classic challenge: layering self-serve into a sales-led motion. The company had Amplitude's own analytics product at its disposal—arguably one of the most sophisticated in the market. But the breakthrough didn't come from dashboard configuration. It came from a hypothesis and counter-hypothesis framework.

Where we disagree, instead of sitting in meetings over and over and trying to get everyone to align, just recognize we're not gonna agree. What's the hypothesis we're gonna go with? What's the strongest counter-hypothesis? Let's pay attention to that. And then the top 10 ones we're most divisive on, we'll just in soft launch pay attention and see where we land.

— Laura Schaffer, VP of Growth at Amplitude

The analytics platform is useful only insofar as it helps you test hypotheses quickly. The tool isn't the strategy. Schaffer's experience at Twilio reinforces this. When her team tested adding qualification questions to the signup flow—expecting conversion to drop—they discovered conversion actually improved by 5%. No complex attribution model predicted that. They shipped the test, watched the data, and learned.

The AI paradox: everyone's excited, few are systematic

Cem Kansu runs product at Duolingo, where the team has integrated AI across the learning experience faster than almost any consumer app. The company's speaking practice feature, powered by LLMs, emerged from a simple insight: they were already testing GPT-3, they knew Duolingo needed better speaking instruction, and the technology suddenly made it possible. No AI strategy deck. No six-month vendor evaluation. Just: "We prototyped a thing where Lily talks to you. We really liked it. We made a feature out of it."

Santiago Savinon, Chief Growth Officer at 99minutos in Mexico, has a bolder vision. He wants every stage of the sales funnel enabled by AI tools. Not because AI is trendy, but because it removes excuses and accelerates autonomy.

Quiero que cada etapa esté habilitada por alguna herramienta de AI, que AI juegue parte en meterle turbo a cada una de esas etapas. Le quitas las excusas al vendedor. Ya tienes un agente de AI programado que te va a resolver cualquier duda que quieras. Yo lo veo como darle al vendedor los poderes de un CEO.

— Santiago Savinon, Chief Growth Officer at 99minutos

Savinon even trained an AI agent on a book he wrote about finding mentors, allowing readers to have personalized conversations with the content rather than passively consuming it. His view: the leaders who don't engage with these tools will be "lejos de la conversación"—far from the conversation.

Yet there's a gap between excitement and execution. Most companies have added ChatGPT to their Slack workspaces. Far fewer have systematically embedded AI into their growth operations the way Duolingo or 99minutos have. The difference isn't access to technology. It's the willingness to prototype, ship, and iterate without waiting for perfect clarity.

Where growth leaders actually disagree: experimentation paralysis

Elena Verna has advised growth teams at Miro, Amplitude, Dropbox, and dozens of other companies. She's seen the same dysfunction repeatedly: teams that treat every initiative as an experiment, creating what she calls "a paralyzing disease."

If every single one of your initiatives that you're doing on growth is an experiment, that's a problem. It's almost like a disease, like a paralyzing disease.

— Elena Verna, Growth Advisor

Verna's argument: not everything needs to be A/B tested. Some changes are strategic bets that require commitment, not incremental validation. Experimentation platforms are valuable, but over-reliance on them can prevent teams from making bold moves.

Laura Schaffer pushes back on this, at least in certain contexts. At Twilio, her team ran aggressive experiments on developer-facing products, knowing that gut instinct often failed in technical environments. The surprise conversion lift from adding signup questions? That only surfaced through testing. For Schaffer, the experimentation muscle is what allows you to move fast and learn, not what slows you down.

The disagreement isn't about whether to experiment. It's about when experimentation becomes a crutch that prevents decisive action. Verna sees teams hiding behind tests to avoid accountability. Schaffer sees teams using tests to discover non-obvious truths. Both are right, in different contexts.

The rebrand trap and other growth tactics that waste time

Elena Verna maintains a list of growth tactics that never work, compiled from years of advisory work. At the top: hiring a growth team before you have product-market fit, and redesigning your marketing site in hopes of boosting acquisition.

Never ever once have I seen a rebrand or redesign, especially of your marketing site, produce good performance results. New CMO comes in designing their website or designing the brand as if it was reflecting of their personal taste, and oftentimes it's promised with our acquisition is going to go up and it never materializes into anything meaningful.

— Elena Verna, Growth Advisor

This is the anti-tool argument taken to its logical extreme. Growth leaders waste enormous resources on initiatives—rebrands, new CRMs, marketing automation platforms—that feel like progress but don't move metrics. The best growth operators are ruthless about cutting this waste. They'd rather ship five small tests with Google Sheets than spend a quarter implementing a personalization engine they don't understand.

Chris Miller's experience at HubSpot validates this. The early growth team didn't win because they had better tools than the sales-led organization. They won because they moved faster, owned outcomes, and weren't afraid to kill what didn't work. "That attitude of saying that every problem is our problem and radical accountability and ownership mentality helped us find opportunities that maybe the business wasn't explicitly asking us to solve."

What the real growth stack looks like in practice

So what do growth leaders who've scaled products to hundreds of millions of users actually rely on?

One analytics platform they deeply understand. Not five dashboards with partial implementations. One tool—whether it's Amplitude, Mixpanel, or even Google Analytics—that the team has configured correctly and uses daily. Bangaly Kaba, who led growth at Instagram to over 1 billion users and now runs product at YouTube, emphasizes "understand work" over "identify, justify, execute." You can't do understand work if your analytics stack is a black box.

What I call the anti-pattern of what we want to do. Someone says, hey, you know what, this would be great to build. Then you go pull data to go justify why that would be great to build. Call that identify, justify, execute. First, you have to really understand from first principles what is actually going on. So understand, identify, execute.

— Bangaly Kaba, Director of Product Management at YouTube

Canonical documentation that travels with the team. Kaba has a framework with five components—vision, skills, incentives, resources, action plan—that he's carried from Facebook to Instagram to Instacart to YouTube. It's not software. It's a mental model, written down, that creates shared understanding across organizations.

Lightweight experimentation infrastructure. This might be an off-the-shelf tool like Eppo or Optimizely, or it might be an internal platform like Twilio built. The key is speed to results, not feature completeness. Schaffer emphasizes that Eppo delivers results quickly, avoiding "annoying prolonged analytics cycles." The tool exists to help you learn, not to create process overhead.

AI agents for specific, high-value tasks. Savinon's vision of AI-enabled sales processes isn't theoretical. He's mapped every step of the sales funnel and is systematically introducing AI tools to accelerate each stage. Duolingo has embedded LLMs into the learning experience itself. These aren't science projects. They're operational bets that compound over time.

Spreadsheets. Lots of spreadsheets. Every growth leader interviewed for this essay mentioned spreadsheets. They're used to track experiments, model scenarios, and sanity-check dashboards. The spreadsheet is the ultimate canonical document: everyone can read it, anyone can edit it, and it forces you to think through your assumptions cell by cell.

The through-line across all of this: simplicity and legibility beat sophistication and complexity. The best growth stacks are the ones that the entire team—product, engineering, marketing, sales—can understand and use. If your growth stack requires a dedicated analyst to interpret, it's probably too complicated.

The real competitive advantage is speed of iteration

Cem Kansu describes Duolingo's product philosophy as "consumer products live and die in the pixels." Meaning: the details matter enormously, but you can only get the details right through iteration. You can't plan your way to pixel-perfect execution. You have to ship, learn, and refine.

If a button that's the dark shade of green versus the light shade of green will make a difference in user behavior, a product manager or a product designer should understand what that means. This is how we build product teams at Duolingo, where everyone should understand pixel-perfect design and why specifically what the user is going to see, how that's going to change user behavior.

— Cem Kansu, CPO at Duolingo

The growth stack that enables this level of iteration isn't the one with the most features. It's the one that gets out of the way. It's the analytics platform that answers your question in 30 seconds instead of 30 minutes. It's the experiment framework that lets you ship a test on Friday afternoon without a three-day code review. It's the documentation that lets a new PM ramp in a week instead of a month.

Chris Miller's insight about HubSpot's early growth team applies broadly: the teams that win are hungry, and they keep getting fed because they deliver results. You can't deliver results if you're waiting for IT to configure your marketing automation platform. You deliver results by using boring, reliable tools that everyone on the team already knows how to use.

The takeaway isn't that sophisticated tools are bad. It's that sophistication should be deployed strategically, in the places where it actually unlocks new capabilities. Duolingo's use of LLMs for speaking practice is sophisticated. Their decision to obsess over button colors is not. But both contribute to growth, and the latter is probably more important than the former.

Growth leaders at Meta, HubSpot, Amplitude, YouTube, and Duolingo have access to the best tools money can buy. They choose not to use most of them. They choose canonical docs, simple dashboards, and relentless iteration. The growth stack they actually use is boring. That's the point.