Why Building Has Never Been Easier in the AI Era | Kevin Scott

Yeah.

You're in a startup. That's kind of your life. It's more about the grind. 99% of it is just a grind.

Yeah. Yeah. Yeah.

It's interesting, you know, how one of the particular areas you and I mentioned earlier, you thinking about is how to reconcile the fact that these models are so general purpose. And we've obviously got an ever-expanding context window so you can tailor them to what you want them to do for your particular use case. But ultimately there's still a huge opportunity in terms of long-term memory retrieval. You can call it maybe fine-tuning. But a lot of it is how do you successfully apply these models to big separate context that could be your organization, it could be your function.

Yeah, I think there's a huge amount of opportunity there. I've been talking about this for a couple of years now. If you just scope down to agents, and you think that the purpose of agents is for humans to be able to delegate increasingly complicated tasks for the agents to go complete autonomously, as much of that complicated task as humanly possible, it's inconceivable that you could do a similar sort of delegation to a human that didn't have a functioning memory.

You have to be able to not necessarily have the entirety of that problem scope in your active memory at any one time, but you have to be able to do retrieval. A biological brain is really good even at imprecise retrieval with mechanisms to drive precision after you've gotten in the ballpark of what you need to retrieve. Agents are going to need to be able to scratch pad their work, remember previous interactions they've had with you, and way more effectively than things like RAG, pull things that they need to solve a problem into context. I think there's a bunch of infrastructure that you need to build there, and even a bunch of application-specific things that you're going to have to build. If you think about biological memory, a bunch of us go to university and we get a bunch of techniques loaded into our brains that help us manage discipline-specific memory. I think you're going to need that in agents. It's going to be task or product specific, and it's not going to just drop out of training a little bit bigger model. Someone's going to have to go do some real work to plumb all of that stuff all the way through.

Yeah, I thought that, I don't know how many of you listened to Sasha on Dwarkesh, I think a week and a half ago, but I thought his articulation of this was super interesting. He said that right now the focus seems to be, can we get these foundation models to out of the box be the world's best knowledge worker for everything, whereas in reality what you really want is a really smart knowledge worker who can go learn about the stuff you want them to do in your company.

Because it doesn't make sense that out of the box somebody would be right at 29 things good at everything, and that's kind of the approach we're taking right now with our whole pre-training, post-training. But really the point of going to university is that you have a bunch of really solid foundations and capabilities that you can then apply in a variety of different contexts. So it's a second thing that we need but we just built half today, I think.

Yep, which are actually by some measures probably more than 50 to 60% of calls within applications according to some of the numbers. How is that open source versus closed source in your opinion going to play out over the coming years?

Yeah, and I look, I love the open source models. I was, I still am, I have infinite amounts of curiosity. Being able to tinker around with stuff and see how it works, I think they're nothing but positive.

Yeah, I think it's pretty clear if you are on or near the frontier that for pre-training at least, we're well past the point where there are enough tokens organically occurring in the world to train bigger and bigger models. It has been the case for quite a while now that the super high capability frontier models that are trained with a ton of compute have a lot of synthetic tokens that they're doing training on. But I also think it's clear to everyone that having experts providing feedback in the post-training part of the process is really critically important. This is a thing I would encourage you all to look at. To the extent that you are doing custom post trains on models yourself, you have to think about where advantage can still exist. It can exist in you understanding your domain and being able to identify experts that you can then use as part of your post-training process to make your particular post-train for your application better. I think that's certainly a thing that we're going to continue to see in a whole bunch of ways. A bunch of this stuff, if you can get to scale, can happen with clever UI design inside of agents. I was on Gemini the other day. Yes, I do use my former employer's product. It's a good model. There's more good stuff coming too, which is the really exciting thing. No one should think that the next announcement any one of us makes is the last great announcement. Stuff's just going to continue to grind and get better. I was using Gemini and I asked it a technical question, and it popped up two completions for the prompt and asked me which one I thought was better. You will have increasingly inventive UI treatments.

Yeah. Yeah. Yeah. But the other thing that's really important when you talk about data, and this may be the most important dimension of data, is you have the data that you're using in your pre and post training part of your infrastructure building, but you also have all the stuff we were talking about a minute ago: how do you hydrate memory? How do you make sure that you've plumbed through all the data sources that you want your models or agents or AI systems to have access to in order to actually solve problems? I think that maybe is the more important data, and it's the place where you're going to have the messier set of constraints. People who have proprietary data right now are on the one hand going to want to be able to use AI systems to get more value out of the proprietary data, but they're going to want to do it in a way that is not leaking a whole bunch of value out of their data.

Yeah. And so that's an interesting set of problems to go solve.

Correct. And so that's the thing I would just sort of encourage everybody, especially right now. The other thing that's happening with these AI systems is with all the coding agent work that's happening and how fast that's getting better, the cost of doing the experiments has never been cheaper.

So do the damned experiments. Try things.

Yeah.

Yeah. Very, I don't think anybody would have predicted what happened.

I mean the other thing too I would just really encourage folks not be precious about the possibility of failure, because one of the things, there's almost this network effect that happens when things get easier, and then we go move faster along those vectors because the thing has gotten easier. We also build up more appetite for more people to try things. I think we have an all-time high right now in the ease of running these experiments, and I think an all-time high in people's willingness to try new stuff. It's dizzying right now. I had a buddy who was the CTO of a game company that runs in six-week sprints, and he was like, 'I went into one sprint thinking that I was completely up to date on AI coding. I went heads down for six weeks to get this stuff done. I pull back up and it's like I feel like I know nothing now. The entire world has changed in six flipping weeks.' I just think the appetite for trying all of this stuff that's changing is super high, which means it's almost this perfect network of experiments and folks who are willing to just look at anything to see whether it's worth something. I love surfing myself, and right now it's like the wave is long, it's kind of perfect, and you just get on that wave and you ride it for as long. You don't have to worry about the other people surfing the wave because if you look back you're going to trip over yourself. You don't want to look too far ahead because you can't really predict what the wave will do. But right now it's fun, and you just focus on your own form and have some fun with it.

I was chatting with Guido just last week about the GIL.

Well, look, I think the two things that you said are good: they need to have agency and they need to think of the thing as a tool that's there to help them tackle the things that they think are important. I think the question to ask is whether or not the AI systems are making people feel more or less empowered. For your kiddo, at 9 years old, it almost certainly has to be empowering to be able to draw a video game into existence. In a certain sense, who cares what the mechanism is? They are accomplishing a thing and they are learning early to be fearless about it. I think kids are going to have the same advantages that kids always have when they're coming of age when a new technology is emerging really quickly, because they just won't be afraid of using it in ambitious ways and they won't have a whole bunch of preconceived notions constraining them. But I think a lot of it is going to be getting back to fundamentals. Do you have good taste about problem selection? Do you really understand how systems work and fit together? Are you really thinking about what you're doing in a service-oriented way? What am I doing with these tools that is of service and of value to my fellow human beings versus just screwing around inside of the system for the sake of it? I think the things that are true for kids are true for all of us. There are certain things for sure that are going to change in pretty dramatic ways, but everything that's changing is also presenting a set of super interesting opportunities for people because there is a positive sum empowerment mechanism at work here. My 17-year-old is a bio nerd, and I remember she asked me a few years ago, 'Do you think being a heart surgeon is a stable job given all the AI?' I was like, 'Yeah, almost for certain. The population's getting older, heart disease isn't going to be cured anytime soon, and even though robotics is getting a lot better, a huge amount of medicine is about human contact and a bunch of super messy stuff that you can't solve entirely with technology, and people don't want solved entirely with technology.' I think those are things too for kids that they ought to be looking for in terms of careers. Anything that you can look at and say, 'This is robotic and repetitious,' those are things that some technology, whether it was AI or not, was going to come get at some point because you've got the dual problem of this thing being irritating to me doing the work, so it's likely also irritating to other people.

You all are maybe doing the most fun thing in the world right now, which is starting companies and building things at the best time that I have ever seen in my career to be building things. I know building things is hard. It's a grind. Just don't lose sight of how special this moment is. I think everybody in this room has the potential to make a massive impact on the world by just being fearless about how you put this technology at work for other people.

Sure. One of the things that I often talk to founders about is that there is this feeling that if they solve this one thing, raise this next round, then all the stuff becomes easier. I often tell them that no, Kevin's playing the same game as you are. Sure, some of the scale might be different, but ultimately if you are in the mode of 'I want to build cool new things, I will push myself to the limit,' then it will be hard, it will be challenging. Whenever a founder is like, 'Oh, if I just get to this next thing it'll become easier,' I'm like, 'It doesn't.' It just doesn't. You have to enjoy the game along the way and also acknowledge that if you're playing the game competitively, it's supposed to be hard.

I've always described myself as a short-term pessimist, long-term optimist. My short-term pessimism is like, 'Everything is awful, it's just awful.' And I carry myself through the day with that spirit. I had a boss, I don't think you'll mind me saying this, Jeff Weiner, who was the CEO of LinkedIn, a dear friend, and I learned so much from him. I had a one-on-one with him one time where he looks at this grumpy piece of engineer who works for him and he's like, 'Dude, you're always unhappy. I'm going to help you reset your hedonic equilibrium.' I was like, 'What are you even talking about, man? I don't want my hedonic equilibrium set. I don't even want to be happy. That's not the first order thing. I want to do meaningful work. Meaningful work is hard. And I'm not going to be happy while I'm doing it, but I will be content.' I think that's all I need to hope for in my life is to be content at doing meaningful things. It's all going to be hard. Learn to accept, enjoy, and appreciate the hardness. It is a privilege that you all get to solve hard problems.

Privilege.

That was another episode of Minus One from the team at South Park Commons. Make sure to subscribe to our show wherever you listen to podcasts and find us on social at South Park Commons. And thanks to our friends at Atomic Growth for their support in bringing this episode to you.