How a 2x IPO Executive Does Product Management

Scott, you have had an incredible career in product management. You joined SendGrid in 2013 as their very first product leader. You scaled the team to over 30PMs. You helped took that company public. You also were the chief, you were the chief product officer for GitLab and you helped take that company public in 2022. So you've had a lot of experience in product management. What is one trait you see in world class product managers that most people overlook?

I would say systems thinking. If you think about a product, it's not just bits and bytes and features, it's a whole user experience that involves the marketing message, marketing and sales, positioning the website and what it reads, the pricing page, the sign up flow, onboarding, the user experience itself, support customer success. You have to consider all these things as a product manager to do a good job because it all has to connect and it has to result in a seamless user experience across all those touch points. And so people who can connect the dots between all these various touch points have a much better chance of driving the intended outcome with the product actions that they take. It's not just again about shipping software, it's about does it lead to the thing that you want? That might be user retention, it might be up tiering from one version to another, it might be a free to paid upgrade, it might be a cost cut, it might be security remediation. These are all outcomes that can be driven through great product management work.

That's a great response Scott. You have had a really impactful career. You actually started your career in sales, if I'm not mistaken, for about eight years and then transitioned into product management. You've had some incredible stints as the first product leader at SendGrid in 2013, helped taking that company public in 2019. You were the chief product officer for GitLab in 2019, helping take that company public as well. What is one belief that you held for years that now with AI you think is challenged or broken?

Yeah, things are changing fast in product due to AI. One area that I think is going to change is the role definition of what product managers do and what some of these complementary roles do. So historically best practice has been for the product manager to prioritize the problem that you're going to solve and decide what's most important to do. Then the designer steps in and helps create the right user experience to solve that chosen problem. And then engineering takes that design and builds it into something that can be delivered to customers at scale in production and those that division of labor has been pretty well set for a couple of decades with AI tools like cursor, lovable, bolt, v0replit, figma, make these all enable less technical people to actually build product experiences. And so I think the lines are going to blur between what a product manager does, what a designer does, what an engineering engineer does, and I don't know where it'll land for the next few years, but I think that one person can do more of these things with AI's assistance. And so I expect role definition and team construct, team construction to change a lot in the next couple of years.

Great. I have a question that I just thought of, that I think I'm going to ask you and I'll ask you. I've heard people talk about how there's pre AI companies and post AI companies and really that ChatGPT magic moment in 2022 was kind of the pre post LLM company with some people saying that before 2022 and ChatGPT you were a SaaS company. And after 2022 or AI company, if you were product manager at a SaaS company, a pre LLM company who was trying to guide their company towards the future in an AI world, where would you start as a product manager?

I would study like hell. Gotta. You gotta learn the ins and outs of AI systems. You have to know how LLMs work, you have to know how to prompt them. You have to understand that LLM AI systems are non deterministic and you have to manage that differently before either the feature passed the test or it didn't. It either behaved like you thought it should or it didn't. With AI systems you have bounds of correctness, basically and you have to decide what those are and you have to think about the product experience differently because you might prompt the LLM twice and get two different responses and so which one were you going to allow? Which ones aren't you? And how can you improve those responses so that you get the range of outcomes you want? And so it's a different way of thinking and it is incumbent upon you and the engineers on your team to really understand how these systems work and be able to deliver a great user experience through them.