Growth is hard. Predictability is earned, not given.

Teams that learn this early compound faster than the ones still treating growth as a collection of tools.

Data and dashboards matter, but they only tell you what already happened. The thing that actually creates predictable growth is the loop: ship → learn → iterate. Over time, that loop turns chaos into pattern.

The most important inputs to that loop rarely come from tools. They come from the teams closest to customers.

• Sales sees patterns in emails and calls that could be automated.
• Marketing spots experiments that might work, if they could run them fast enough.
• Product keeps seeing the same drop-offs in onboarding and activation, but needs a quick way to test fixes.

The signal is there. What's missing is leverage.

Most teams try to close that gap with more tooling. No-code and automation platforms help with simple workflows and quick wins. They remove friction at the edges. But they hit a ceiling as soon as you need custom logic, deeper data access, or anything that doesn't fit neatly into templates.

That's where an engineer changes the game. An engineer turns GTM into something you can actually iterate on like a product.

This isn't theoretical.

In a recent conversation, Jeanne DeWitt Grosser, ex-Chief Business Officer at Stripe and now COO at Vercel, described how GTM stopped being treated as a static function and started being treated as a system that could be rebuilt.

At Stripe, the team tried to do this as early as 2017. They attempted to automatically personalize outbound based on company data. Even with world-class data scientists, it failed. Error rates were too high. The infrastructure simply wasn't ready.

Today, that same idea works.

Not because the strategy changed, but because AI made previously impossible workflows viable.

That shift is most obvious in how Vercel runs GTM now.

At Vercel, a single GTM engineer built an internal AI system that spans inbound leads, outbound follow-ups, and deal-loss analysis. When a lead comes in, the system enriches the account, qualifies intent, drafts tailored outreach, and routes the deal without a human touching it.

The same system reviews every lost deal by reading emails, call transcripts, and internal Slack threads. In one case, a salesperson believed a deal was lost on pricing. The AI surfaced the real issue: the team never spoke to the budget owner, and the customer didn't believe the ROI narrative.

That insight changed how Vercel runs sales calls. The system now sends live alerts like, "You're halfway through the process and haven't engaged a budget decision-maker yet."

The entire setup costs on the order of $1,000 per year to run. It replaced what previously required a large sales support team. Those people weren't laid off. They moved into higher-leverage work, while the remaining salesperson became dramatically more effective.

This wasn't achieved by buying better GTM software.

It happened because an engineer could take messy human insight, wire logic end-to-end, and encode Vercel's GTM strategy directly into software.

We see the same pattern internally at Browserbase.

We have built Slack bots that sit directly inside GTM workflows. They generate example code snippets for customers, debug incoming technical questions, and prepare account-specific context before sales calls.

These bots are connected to our internal tooling, documentation, and live product context, which means they don't just answer questions. They shorten feedback loops. We can test messaging, validate objections, and spot friction in real customer conversations within minutes, not hours.

None of this came from buying a new GTM tool. It came from engineers embedding themselves inside the growth loop and turning insight into software.

This is what a GTM Engineer actually is.

Not a marketer who learned Zapier.

Not sales ops with better dashboards.

But an engineer embedded inside GTM, whose job is to turn insight into software.

When GTM becomes a system you can rebuild, someone has to build it.

Teams that realize this early compound faster than the ones still treating growth as a collection of tools.