00:37:59 · Teresa Carlson
And I believe Anthropic working with defense is a good example of if we would have had some framework that they were working off of, I think, you know, I don't know if the outcome would have been different, but it would have helped them as both the government and the commercial entity to have that conversation, and we just haven't gotten there. We still have a patchwork going on in these AI regulations, which makes it very hard on our startup community. But on just scale, I will say I used to tell my team at AWS, strap in because you won't see anything like this. And with AI, I'm like, this is a double or triple strap in. It's like nets. Prediction that I think that you'll see with AI is you're seeing a lot of spend, a lot of spend happening. And it needs to, you know, both with the model development and also just with customers' adoption, they're spending and they're using a lot of tokens. And I've shared with some of our founders that I believe you'll see what I call the optimization point, though. And we saw it with cloud, where there was a lot of spend. And then customers said, wait a minute, I want to optimize how I use. So this is where you saw the cloud providers reduce. They would have a model where we reduce pricing. Our motto was we wanted to decrease our price point. And then that would cause customers to spend less, but then they would adopt new services. So there was kind of a it was a real balance because we wanted that. We wanted the spend to basic compute and storage to go down so they could take advantage of other things like, you know, database services, streaming, AI, the other tools that we created, we wanted them to take on new services. So the spend over time actually kept going up, but you needed to reduce certain spend to see others go up because it's not an unlimited budget out there. And I do believe with cloud, you will see still a huge burst of spend, but then larger companies will say, well, wait a minute now, how do I optimize that spend? And what tokens, how do I really need to use it? And what workloads and workflows are the most critical to the success of my business with AI? Cloud evolved into a multi cloud environment, so you don't have dependency on one or the other and you can
00:40:29 · Molly O'Shea
[00:40:29] S1: have your data in multiple places in case there's an outage or something like Do you think something similar will occur in AI? Do you think people will stick to one model in the future or do you think we'll keep separate use cases for different things?
00:40:45 · Teresa Carlson
[00:40:45] S0: I think we'll keep separate use cases. You can't change human behavior. And human behavior, it comes to buying tech, is they're constantly looking. It's one of the it's a great thing for the industry, the tech industry overall. It's not always great for commercial industry. You see over and over companies that buy too much tech. And what I mean, they want to experiment a lot. And if they, then they kind of say, woah, I got to back to optimization. I got to look at all of the technology services I own. And am I really using them? This is just tech debt. This has been going on for years. And it takes companies a long time to get rid of their tech debt. And also if they've created an application on a platform, and then they're like, well, now I've got to move it. And that becomes very challenging, especially for large enterprises or governments. But in AI, yeah, I predict that we are so early in what is being created and how quickly it's being created. I think our, first of all, we're trouble processing just what's happened in the last few years, but it's even going to go faster. So you're going to see more models created, you're going to see more solutions created, more adoption because as humans, we're going to learn as leaders, we're going to really determine what's the best model we use with our employees to teach them how to take on AI. We're very early with that. Most companies are still trying to determine, can I use it at all? How do I teach my employees? Am I okay with them using AI? I mean, even my team still is like, are you okay? I'm like, go use it. You have to make sure that you are doing background checking, that you're checking your data. I mean, it still hallucinates a lot and it tells you even, hey, check my work. So we're still at the point we need to do a lot of checking, but you've got to get your employees really okay using it. And now as new employees come online, they've got to have a lot of agency to both learn AI and understand how they manage with AI. So there's kind of two different models. But I think you're going to see workloads go in all different directions with all different companies' uses. And it's going to be who's the leader at the time.
00:43:10 · Molly O'Shea
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00:45:33 · Teresa Carlson
[00:45:33] S0: I think it's very important. One of the mistakes that I see is when companies have great technology but they don't determine who's going to use it. And what legitimizes you is usage of those tools and the customer saying it's a good tool. And we over focus, In fact, I have such a deep appreciation for Andy for challenging me on some of these concepts because when I first got to AWS, I had kind of been taught at Microsoft, we did not get customers to speak out a lot. And we always called it back then the Washington Post test because they didn't want to be on the front page of the Washington Post if something went bad. And again, back to sometimes they would overbuy or a project. I could point to so many tech projects that went wrong when it was kind of just a license based model because they defined their requirements poorly a lot of times. I mean, when you're in that, I'm not saying government means to do this, but if you're defining a requirement, just like us sitting here today, today you have a requirement about how you run your podcast. In two months, there may be a new technology and you're like, woah, I got to change up the way I'm doing this or, deploying my podcast or how I'm marketing my podcast. And you want to have flexibility in how you're doing something. The government back then had no flexibility. They would define something, they would put it out in an RFP, and they would buy that thing. Within two months, it might be outdated. So after two months, something new had come up and they're like, oh, this project's not working. With cloud, that actually changed the game because you had a lot more flexibility in how you would define that. But with startups, it is critical with startups that they define what they're selling, who the user of that tool is going to be, and they get great legitimacy. And with Andy, he questioned me. He said, look, you got to get references. You got to get people to talk. And I said, Andy, in government people want to tell the story. He goes, yeah, they will. And I was like, oh, I was concerned because was like, how do we get customers to actually say good things about what they're doing? And if you go back and look at all of my public sector summits in re:Invent, I had so many customers that wanted to tell their stories. And I really appreciated that he pushed me on making that a reality.
00:48:07 · Teresa Carlson
And we decided that our superpower was not really what we did, but what our customer did. And getting the customer to say that cloud computing was game changing was what made all the difference. So I really think these startups should over focus. Once they have what they're doing, spend time getting the right customers, getting the customer to say you're doing a good job, going after those contracts, really working to make sure that they have scale and growth in revenue because, again, it legitimizes them that you have customer usage that you can go back and say, okay, CIA, CIA is using it, NRO or JP Morgan or Goldman Sachs. You can use that as a reference point over and over. I could continue to talk to you about all these topics for hours, but we need to talk about the General Catalyst Institute. So why did you and Hamant decide to launch this? Well, again, another great leader that I've had the opportunity to work with in my career, just so blessed. And HT is such a great leader. He is a goat of investing. I used to say Andy and Jeff were the goat in retail and cloud, and now I've got this amazing leader in investing. And, you know, I got to meet Himat because I was recruited to be on one of the boards that they started called Chemure. And I got to know him there and he said, Oh, you should meet some of team. And then I met Paul Kwan, who runs our Global Resilience. I was like, Woah, they're doing public sector and all these industries I love. And Hemant said, you should be an advisor to us. They have this program that they bring former executives on as advisors. So I worked for about eighteen months doing advisement and helped do four investments. I brought some investments to the table. So I kind of learned the investment side. And then Hamant and I talked about, he goes, you know, you should think about coming on here full time. And I was like, do what? Because I don't feel like my superpower is investment. I am not, like I see, I'm more of a builder. I like to help companies build versus like that day to day investment is not what kind of gets me out of bed every day. But I love seeing what we invest in and making them successful. And one of the things we talked about was have a general callous. They're amazing. They raise a lot of money. They invest in amazing founders, but most of these companies are in highly regulated industries.