Should you build teams of deep specialists or versatile generalists?
Experts
Deep expertise wins. Hire the best person for each specific function.
Generalists are more adaptable. In startups, you need people who can wear many hats.
The UX Research reckoning is here | Judd Antin (Airbnb, Meta)
So see where they go. It's actually pretty simple. Most researchers are deeper in one than the other, and sometimes you can make up for those five tools with the team. So you have experts who are t-shaped, but maybe deeper in one or several of those ways.
Actually, I'm curious what you think, because here's the number one thing I heard when I joined Airbnb and you were there, is I did it a quick listening tour where I talked to a bunch of product people. And they all said the same thing. They were like, "Listen, we have all these different people throwing insights over the transom. And it's great.
How to build a high-performing growth team | Adam Fishman (Patreon, Lyft, Imperfect Foods)
I would bucket that type of person into a specialist. And one of the archetypes that I talk about is this idea of the painter versus the architect versus the surgeon. And the surgeon is your precision person.
And so when you think about it, there are four main components to the growth competency model. And the four big components or buckets are growth execution, customer knowledge, growth strategy, and then the last one is communication and influence. And each of those has three really specific skills.
How Shopify builds a high-intensity culture | Farhan Thawar (VP and Head of Eng)
Yeah, so there's a few things, right? One is, the more context you can fit in your head around a code base. And you can never really fit all of Shopify in your head, because it's a big complicated set of tools we give to merchants.
Lessons from working with 600+ YC startups | Gustaf Alströmer (Y Combinator, Airbnb)
In terms of founders, I've seen everything, right? I've seen people having some domain expertise starting a company and really succeeding. I've seen people who had no domain expertise and learned everything they need to know. Maybe they partnered up with someone who had a domain expertise and then succeeded.
Gustaf Alströmer Yeah, I would say if you were working on a software company and even some of the hardware companies, that's probably generally true. Absolutely. It's much more valuable to have that background than to have this specific domain expertise background. Those are complimentary.
From managing people to managing AI: The leadership skills everyone needs now | Julie Zhuo
They have to learn, so initially things take longer, takes a little bit of extra time, versus if you did slot in a front-end specialist and this is a front end project. It probably would've gone a little bit shorter, but in the long run that investment really pays off because now you have a lot more people who are, again, a little more well rounded...
Unconventional product lessons from Binance, N26, Google, more | Mayur Kamat (CPO at N26)
Whereas in product, you largely find that it's a combination of data and ideas and stuff, and anybody thinks they can... The moment you build experimentation, you'll now make it scientific, right? Now, somebody comes up with an idea, say, that's a bad idea.
Building a magical AI code editor used by over 1m developers in 4 months: Inside Windsurf
But now one of the crazy things is that the domain specialists now have access to build the tools that they ultimately wanted, which is actually crazy. If you think about why were these software companies able to exist these vertical software companies, the reason is because they had many features.
Building Lovable: $10M ARR in 60 days with 15 people | Anton Osika (CEO and co-founder)
Doing a bit of everything. Being a generalist, I think much more important than it used to be. If I'm putting together a product team today, I would really obsess about getting as many skill sets as possible for each person I hire.
I mean doing a bit of everything. Being a generalist is I think much more important than it used to be. And if I'm putting together a product team today, I would re-obsess about getting as many skill sets as possible for each person I hire.
Bending the universe in your favor | Claire Vo (LaunchDarkly, Color, Optimizely, ChatPRD)
And this is the other advice I might give particular to PMs. PM is such a generalist role, it's okay to go a little left and a little right to go up. I took this marketing growth role. That was actually my first director role. It wasn't only for product, it was for marketing.
And so I do think it's really important to have a specific and measurable career ladder, especially at the senior levels. I often find that they're very soft. They're hires and manages multiple departments or takes in cross-functional stakeholder feedback. Those are just not tractable, specific things.
How to build a powerful marketing machine | Emily Kramer (Asana, Carta, MKT1)
And there's such a range of being a deep specialist. You have people that are just deep SEO specialists or deep paid search specialists or writers and they love to write and they write long form content and that's definitely a part of what marketing does, but it's a specialty for sure.
They're definitely more of the generalist function. And so sometimes that's a great answer. You need a product marketer, but sometimes you need someone that's scrappier in different areas. Another one of my frameworks is we start with doing these seal engine, what are the biggest problems? What are the biggest things that if you did, you would driv...
“Dumbest idea I’ve heard” to $100M ARR: Inside the rise of Gamma | Grant Lee (co-founder)
Just one thing to add is that's where we are at today. I think being adaptable means that certain things may evolve and change and how the player coach model evolves and maybe in certain functions or as the team scales, that's not going to be practical everywhere.
How we restructured Airtable's entire org for AI | Howie Liu (co-founder and CEO)
And if not, how do you add in the right human workflow for approval and review, and the reprompting and so on? So just so many different design decisions, and you need somebody who can really think full-stack about that kind of product and is not overwhelmed by that kind of open-endedness, but relishes in it.
You could take that boot camp. You could do like some coding exercises on the side, et cetera. The point there is that sometimes I think we treat these disciplines like hard, hard skills that if you're already halfway into your career and you're not already an engineer, if you're not already a designer, okay, well, you can never be one.
Scale AI CEO on Meta's $14B deal, scaling Uber Eats to $80B, & what frontier labs are building next
One of the main stories I've been hearing is, and I've had a bunch of CEOs from this space on the podcast, is that there's been this big shift from the way, from what Scale had pioneered and had been doing for a long time, which is generalists, low-cost labor training.
It is definitely not going to happen in the next year. The idea that it happens in the next two years I think is very far-fetched, but nothing's impossible here. And long-term, I think that if you go back through, I don't know, pessimist archive or whatever, these accounts that post, the radio was invented and then all of this will be eliminated.
OpenAI’s CPO on how AI changes must-have skills, moats, coding, startup playbooks, more | Kevin Weil
Yeah. I don't know, maybe this is one of those things I believe that most people don't believe, but I actually think chat is an amazing interface because it's so versatile. People tend to go, "Chat. Yeah. We'll figure out something better." And I think it's incredibly universal because it is the way we talk.
Vision, conviction, and hype: How to build 0 to 1 inside a company | Mihika Kapoor (Product, Figma)
And so, ultimately, it grew into this conference that lasted many years, brought together folks from originally around the country, then more recently around the world. And ultimately, did live under that broader organization.
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At our Series A, we hired a specialist ML engineer. They built amazing models but couldn't help with anything else. At that stage, we needed generalists.