The Exchanges

Q to cultivate a sense of playfulness and make a mess. Overall, it's a fascinating conversation with someone who has been at the forefront of some of the most important shifts of technology, Over the past few decades. And now, please enjoy my conversation with Sam. Maybe we can kick it off. I I'm curious, what is the last 30 years of building software taught you about market timing and technology?

A The one thing that I've learned that's like an indelible lesson is that the things that are worth doing are really uncomfortable. The right time to do something is when you have that like feeling in the pit of your stomach that's like, oh, this is a great idea and it's going to suck to build this because nothing's ready yet and there's all these problems. Like we had that problem With gdocs for sure when we did rightly originally and like JavaScript was there, but it was buggy and not standardized and there weren't any frameworks and there weren't any tools and the cloud didn't really exist. The mistake people make the most is, you know, they'll be like, well, that's a cool idea, but it's really hard. Let's wait three years till it's easier. When it's easier, there'll be a million people doing it and there's path dependencies in there and stuff like that. So I see that lesson all over the place right now with AI. Honestly, there's plenty of legitimate criticisms about what does and doesn't work right now. But, like, none of those really seem to matter that much to me. Like, they all just seem like engineering problems to be solved, and you can kind of see them evolving over time. Last year, the issue was it's expensive, it's slow, token windows aren't that big, whatever, whatever, you know, over time. Hallucinations are still a problem, but the grounding problem is getting deal…

AI assessment note: “The right time to do something is when you have that like feeling”

Q You mentioned sort of the topic of you need technical taste. What do you mean by technical taste? Is it a sensibility that you find a lot of people develop?

A Technical judgment may be one way to say it, but I do think there's like an aspect of taste Any of us who've done some kind of, any kind of technology, hardware, software, whatever, for a long period of time, you kind of collect a bag of heuristics. You kind of know what does work and what doesn't work. Technical taste is like, how well have you consolidated that set of experiences and heuristics into judgment that you can apply accurately when you see new things? I've got all kinds of, you know, weird things in my head. Like, you know, at one point I figured out that most of the successful software projects I've seen Have the same feeling of sort of slightly out of control, but not too much of a disaster as chasing a three year old down a slight incline in a park. You're not really in control of the situation, but it's also like not crazy out of control either. That's a little ass little particle of technical taste where like I can look at a team and look at the code base and see how they're executing. And I can tell whether they're failing to the overly architected or under architected side of things by that feel like you could talk to a team and they're panicking. And they don't have good answers to some questions and, you know, they're sort of afraid of their code base. That's a bad technical smell, right? You know, they're in trouble. And you can sometimes do that for larg…

AI assessment note: “Technical taste is like, how well have you consolidated that set of experiences and heuristics”

Q When you think of the characteristics of the most talented engineers that you work with today, what are the characteristics? And do you think in five years they will be the same?

A Some characteristics never change. I think this is kind of goes with the technical taste thing. Sometimes you just, you know, you meet people and you're just like, yeah, that there's something about that Person that they're just a really good, they think lucidly in a certain way about engineering problems. They tend to be, I think, people who are simplifiers more than complexifiers. I think that's always, that's a constant with good engineers. Many people have said this. I think it's very true. You know, good engineers, they're lucid. They can look at a problem and kind of reduce it to its minimal parts and think their way through very carefully and simplify it. That's always a good characteristic. People get a little bit overly attached to actual Technical skills. I remember when I was in my twenties, I started off in Pascal and I was really worried because C seemed to be taking over and I wasn't a C programmer. I needed to learn C. It turns out to be like a totally stupid thing to worry about. I wound up writing code in like 10 languages over the course of my career. That's not the interesting part of being an engineer. The interesting part was being able to think about technical problems and simplify them and break them down and work your way through them and stuff like that. If you can pound out a lot of Python or Rust or whatever it is, That you're good at. Like, you can f…

AI assessment note: “Some characteristics never change... they tend to be, I think, people who are simplifiers”

Q So then what do you think you should do if you're a pediatrician?

A I think there are plenty of practices that this will come to late. But like if I were a physician today, I would start to, I would probably start to look for ways that I could use AI to de-bias myself and to improve my clinical decision making. That's a hard process, right? Like there's licensing and regulatory issues that get in the way of that. Everyone's biased. Everyone makes mistakes. Everyone misses stuff. And if you're a physician, any physician, you know, that has a significant impact on a human being if you do that. And so you want to be understanding and educated in Those tools, I think, when they're ready and when they're better, we already have isolated diagnostic tools that are better than humans. There's breast cancer detection and things like that that are better than humans at spotting, you know, from like a radiology slide. If I were practicing, I would at least want to start to understand when can I enhance my decision making with that kind of tool? When is it ready? What can I do? So these days, there's a study I saw a while ago that compared like an AI based system, AI only physician only and AI plus physician An AI plus physician did worse than AI only. The physician kind of dragged the AI down because they didn't trust it. I think the kind of the point of it is if you disagree with the AI, if the AI is more accurate than you are and you disagree with it ab…

AI assessment note: “I would probably start to look for ways that I could use AI”

Q If the future unfolds in that way, who are the most likely to capture most of the value in this transition?

A It's the same question that you had to go ask in, you know, in 1998 about the internet, right? And nobody had a good answer. I mean, a couple of people had good answers and they're all worth Two hundred billion dollars. Now you kind of try to do this stuff from a mixture of historical precedent and first principles. So what are, where are the network effects? What's defendable? What's, you know, what's likely to be valuable? I keep coming back to personal data. The thing that's probably valuable is if you have an experience that's personalized to you, that is trustworthy. So I trust it to not leak data out that I don't want it to leak. And I have ideas about how that might work. Then you're going to spend a lot of time. And if that experience That agent or assistant or whatever it is, can create custom experiences for you on demand. It's the collection of the best hundred executives in the world. That's where you're gonna spend all your time. Imagine if you were one of those people who was worth two hundred billion dollars or whatever. If you could hire the hundred best experts in any domain anywhere in the world, and they all got together, they all worked along, worked together really well, and you had a chief of staff that managed them, and there's never bullshit and politics and whatever, like, How much time are you doing when you're looking at a screen? You're not doing a w…

AI assessment note: “It's kind of the holders of the data and the companies that we can trust”

Q Maybe you could talk at a slightly more granular level. How did you figure out what to do in what order?

A One of the early things we, we made this deal, I think with the user, we made this bargain with the user, which was We will not fight the war of features that everybody had been fighting and failing against Microsoft. So we're not going to implement everything. But what we'll give you is this really stripped down thing that's really convenient in certain ways. And to the large degree, like we won that, you know, that bet worked. People were willing to trade convenience and speed for a lot of the features. But there's one feature that we kept getting asked about in the early days, which I didn't understand, which was word count. And I was just like, why the hell? Like, that was like, you know, number 900 on my list of things to add back in. Finally, I realized, oh, a bunch of our early adopters are students, and they need to know how many words are in their essays. So like, that's actually a really important feature for adoption. So we put it in because it was easy to do. To be honest, like, I'm a little bit sad at the state of Gdocs these days because I think they're losing sight of the idea that, you know, they're kind of turning it into Office and like making it very complicated to use again. And I find lots of things broken in it when I use it. Like most of Google's apps, I think, are getting very heavyweight Broken now, which is kind of sad to see.

AI assessment note: “So like, that's actually a really important feature for adoption. So we put it in”