Delivery Isn't Free

Petra Wille (0:04)

Hi folks, this is All Things Product with Petra Wille and Teresa Torres. And we're so happy you're here.

Teresa, I know you will love what I say next. Now with AI, delivery is basically free. It's so cheap, it's basically free. It doesn't cost us a dime to develop software these days. Shoot!

It's what we hear about these days in all our calls, right?

Teresa Torres (0:41)

I mean, when I look at my AI bills, it's certainly not free.

Petra Wille (0:46)

No.

Teresa Torres (0:47)

Yeah, let's unpack this. So I'm seeing this everywhere, is delivery free? Now that delivery is free, complete the sentence.

I don't think delivery is free. I don't think delivery will ever be free. I think delivery is getting cheaper, but not all delivery is getting cheaper. So I think there's a couple of ways I want to unpack this.

Petra Wille (1:09)

Yeah.

Teresa Torres (1:11)

The first thing is building a single thing is getting significantly cheaper. I think that is true.

But if the way companies respond to building a single thing, being cheaper means they build lots more things, then delivery is not going to stay cheaper. Because let's just think this through. We have a product. It's a brand new zero to one product. We launch MVP. So the minimum product that we think will get us a customer. It has one feature. Now delivery is free. So we add five features in week one. Now it has six features. Now in week two, we add five more features. Now it has 11 features. Now in week three, we add five more features. It has 16 features. Here's what's happening in those three weeks. We just did this very quickly. What's happening in those three weeks is feature one was easy to build. There's no code base. It was super easy to build.

Feature two is a little harder to build, but it's still easy to build. By the time you're getting to feature seven, feature 10, feature 12, feature 15, your data model looks like a Frankenstein strategy.

Because you're using coding agents to build it with almost no engineering insight because you expect engineering to be free. You have spaghetti code everywhere. You've rewritten the same code 17 times, which means when you want to change something, you're changing it in 17 places.

Petra Wille (2:42)

Performance already down.

Teresa Torres (2:44)

Yeah, your performance of your product is probably terrible from a speed and performance standpoint. The maintainability of your code is terrible. But sure, we got 15 features for free. And I think this is like if you don't understand engineering, it's really easy to naively think coding agents makes everything free.

Coding agents makes things faster. That is certainly true.

Petra Wille (3:10)

And experimentation easier.

Teresa Torres (3:12)

And it allows us to create throw away code very easily. But we can't mix those things up. And actually, I really love the SVPG folks are starting to use this phrase build to earn versus build to learn.

Petra Wille (3:29)

And there's this idea of like, yes, we love it.

Teresa Torres (3:31)

Build to learn is we're going to write code to build an interactive prototype that we're going to put in front of people. And that is dirt cheap today.

And good long throw it away.

Petra Wille (3:42)

Yes.

Teresa Torres (3:43)

Right. As long as we throw it away.

When we build to earn, now we know what to build. We're building so that a paying customer buys it. We still need to care about all those engineering principles we've always cared about, which is maintainability of our code, non-functional requirements, as we would call them. Reusability of our code, the cost to maintain that code, the security of that code, how well that code scales. And can AI help with us? Yes.

Is it free?

No way. Like not even close.

And I actually think...

Petra Wille (4:18)

And so that you would still need a skilled engineering team...

Teresa Torres (4:23)

Absolutely.

Petra Wille (4:23)

observing and overlooking what AI does.

Teresa Torres (4:26)

Absolutely. And it takes time.

Petra Wille (4:30)

Yeah.

Teresa Torres (4:30)

It takes time. Like, AI can generate a lot of code really quickly. We can get more sophisticated in our AI reviews of that code. But it probably still requires a human to look at it and say, is this the architecture that's going to grow, given our company's vision of where we're going?

Right? And like, maybe, here's the challenge with AI episodes. Like, maybe in six months, I'm going to regret saying this because AI is going to get really good at understanding your product vision and creating the perfect code base that scales infinitely.