Matteo Tittarelli and a co-speaker explain how GTM engineers act as force multipliers, letting individual contributors do the work of ten people. They point to Anthropic and OpenAI hiring both engineers and account executives at scale, and warn that storing GTM knowledge in chat histories creates the same knowledge-loss problem data-science teams faced with notebooks.
But I think the way I think about GTM engineering and sort of like this workflow is that they're a force multiplier.
And you mentioned before that, you know, like now you, like what you're hyped about GTM engineering is that now all your team members can own end-to-end workflows. So does that mean that, you know, GTM engineering has literally unlocked the capabilities for one person to do the work of 10? Or do you reckon that's the wrong framing?
I think that can definitely be, again, like depending on the size of your company, like absolutely. You can have a person running an army of agents that can do multiple things. But I think if you look at the fastest growing companies in the world, and I think of two, like Anthropic and OpenAI, they're hiring for two profiles. They're basically telling you, you don't need engineers, and yet they're hiring for a ton of engineers. And they tell you that you don't need go-to-market people because AI can do it, and they're hiring a ton of account executives. And so I think the way I think about GTM engineering and sort of like this workflow is that they're a force multiplier. It's more like, hey, can we double the output of a person? Can we 3x what that person's output expected was, the expected output was? And I think if you think about it that way, you're building in a scalable way. Because what is the key man risk then? Like if you have one person really doing the job of 10, sure, that person might be making half a million dollars, a million dollars a year, and that's great. But like if that person leaves, you basically have, like it's hard for you to just repeat that motion. And so The way I think about it is like you still need to build a team. You still need to have different expertise and diversity of thought within your go-to-market team. But I think again, like everybody can just do more. And so the expectations are higher. Sure, you might need 5 people instead of 15. But I think of it as like, it's something that we should be democratizing. Having been in the developer tools and data space, it's something that I've seen like, sort of like maybe 5 years ago when there was a problem, like everybody was hiring data scientists. But data scientists like live on their notebook. And so like, if you know anything about data science, they have these notebooks that run locally, like Jupyter and whatnot. And those are a ton of just like files that are sitting on their laptops. And so when data scientists like used to leave a company, it was a big mess because they were leaving nothing behind. I think the same thing is true for go-to-market engineers because like a lot of what they do is in their Claude chat or in their ChatGPT chat. And so now like what we're doing is actually going back to, you know, pure developer workflows where my team is going to hate me for it, but like, you we're gonna write a ton of Markdown files in a GitHub repo and everybody's just gonna work from a central place where everything that you do as knowledge, like, should be stored and accessible for anybody else, like, that's gonna join or leave the company.
Roll Claude out from one laptop to the whole GTM org | Sam Gong & Zach Vidibor
