Avoiding ‘team debt’ by building small and smart (w/ Matt Cook)

Matt, great to have you on again.

It has been a few months and already so much has changed.

So I'm really keen to talk to you about what you are seeing out there on the market.

So Matt, I've always thought that you have this really great bird's eye view of the engineering market that you view through the talent side.

And so much is changing so quickly for engineers, engineering teams, how engineering gets done.

And I feel you've always had this great ability to spot the trends and the upcoming issues and synthesize them.

So you just sent me this great list of things that you're seeing and themes.

And the first one of those that I thought we could jump into is team debt.

What do you mean by that?

Team debt is obviously a spin on the idea of tech debt.

I think tech debt is becoming less of a concern for companies more generally with AI and how engineering teams can kind of attack at or attack tech debt and sort of deal with it the way i've heard it described is like the the interest rates are just very low on tech debt and so um it's much easier to take out and deal with team debt i don't know if it's like a direct replacement for it but it's basically the the same idea and i think it's becoming more of a common issue in startups now where Ultimately, they're faced with problems that they need to solve or they're faced with jobs that need to get done or tasks that need to get done.

And they're immediately jumping to, oh, we'll hire a human to do that job or do that task or we'll build out a team to do that job, do that task, achieve that outcome.

And as the models evolve, as teams or companies get better at working with AI, as they build out their own AI infrastructure internally and try and become more AI native, they ultimately, and sometimes in the literal sense, but they ultimately make these hires or these teams redundant or semi-redundant because the tech that comes in in three months or six months, or even the tech that's here now that they haven't quite been able to leverage to its full potential yet starts to almost replace or solve the problems that they're hiring the teams to solve.

And I think a lot of startups are either about to be left with or are already left with teams that have been put together to solve problems that really just won't exist

in the near future.

I've talked on previous episodes of the podcast of the half-life of everything getting shorter.

I'm not sure if this fits into the debt metaphor or not, but I guess what I'm seeing is that the team structures that you have, that you have inherited from an earlier stage of the company, or even that you are hiring into right now because you haven't updated your mental model quickly enough, are becoming obsolete.

And by being obsolete, they become a form of organizational debt that you then need to resolve.

And generally, I guess the obsolescence comes in two related flavors.

One is that your team is bigger than it needs to be, right? Because if you're doing things right, if you're AI native, then you have more leverage.

So you have this sort of hangover of a team that's bigger than it needs to be, but also it's the wrong shape, right? It might have the wrong sorts of roles or even the wrong sorts of people for what you're trying to achieve now.

Yeah, 100%.

As any team grows, there are problems that are introduced in those teams, normal problems, just human bottlenecks.

You need to have meetings for certain things and handovers, that kind of stuff.

And because those teams are solving a problem or they exist to solve a problem that really maybe could or should be solved with AI or will be solved with AI in six months, 12 months or whatever, the more you sort of diverge down this path of building that team, scaling that team, building processes that surround that team, actually getting that team to work super efficiently.

Matt, great to have you on again.

It has been a few months and already so much has changed.

So I'm really keen to talk to you about what you are seeing out there on the market.

So Matt, I've always thought that you have this really great bird's eye view of the engineering market that you view through the talent side.

And so much is changing so quickly for engineers, engineering teams, how engineering gets done.

And I feel you've always had this great ability to spot the trends and the upcoming issues and synthesize them.

So you just sent me this great list of things that you're seeing and themes.

And the first one of those that I thought we could jump into is team debt.

What do you mean by that?

Team debt is obviously a spin on the idea of tech debt.

I think tech debt is becoming less of a concern for companies more generally with AI and how engineering teams can kind of attack at or attack tech debt and sort of deal with it the way i've heard it described is like the the interest rates are just very low on tech debt and so um it's much easier to take out and deal with team debt i don't know if it's like a direct replacement for it but it's basically the the same idea and i think it's becoming more of a common issue in startups now where Ultimately, they're faced with problems that they need to solve or they're faced with jobs that need to get done or tasks that need to get done.

And they're immediately jumping to, oh, we'll hire a human to do that job or do that task or we'll build out a team to do that job, do that task, achieve that outcome.

And as the models evolve, as teams or companies get better at working with AI, as they build out their own AI infrastructure internally and try and become more AI native, they ultimately, and sometimes in the literal sense, but they ultimately make these hires or these teams redundant or semi-redundant because the tech that comes in in three months or six months, or even the tech that's here now that they haven't quite been able to leverage to its full potential yet starts to almost replace or solve the problems that they're hiring the teams to solve.

And I think a lot of startups are either about to be left with or are already left with teams that have been put together to solve problems that really just won't exist

in the near future.

I've talked on previous episodes of the podcast of the half-life of everything getting shorter.

I'm not sure if this fits into the debt metaphor or not, but I guess what I'm seeing is that the team structures that you have, that you have inherited from an earlier stage of the company, or even that you are hiring into right now because you haven't updated your mental model quickly enough, are becoming obsolete.

And by being obsolete, they become a form of organizational debt that you then need to resolve.

And generally, I guess the obsolescence comes in two related flavors.

One is that your team is bigger than it needs to be, right? Because if you're doing things right, if you're AI native, then you have more leverage.

So you have this sort of hangover of a team that's bigger than it needs to be, but also it's the wrong shape, right? It might have the wrong sorts of roles or even the wrong sorts of people for what you're trying to achieve now.

Yeah, 100%.

As any team grows, there are problems that are introduced in those teams, normal problems, just human bottlenecks.

You need to have meetings for certain things and handovers, that kind of stuff.

And because those teams are solving a problem or they exist to solve a problem that really maybe could or should be solved with AI or will be solved with AI in six months, 12 months or whatever, the more you sort of diverge down this path of building that team, scaling that team, building processes that surround that team, actually getting that team to work super efficiently.