The Exchanges

Q happens immediately. It's not something that, you know, the Wall Street types, for instance, will be like thrilled To know that it's just going to take a while, because they think in quarters. So do you think we're there's a risk here and in like within the next few years, sort of the public perception of this technology turning a little bit because of the incremental incremental nature of it?

A I think it would be a possibility if, and it's a huge if, if the technology wasn't poised to improve. So if, if what we, if you believe that what we have today is pretty much what we're going to have to work with, with only incremental small improvements over the next three, five years, you know, then I suspect that, you know, folks will feel like, you know, the, the, the promise on this occasion hasn't been delivered on. But, you know, technology tends to follow an S curve over time. And, you know, you, ah, you get to the top right hand corner of that S curve, and you end up with the technology, with the capability, and you get these, you know, just decreasing improvements over time. Um, you never really know where you're at on the S curve until you're looking backwards. And so it's kind of hard to judge where we're at. I think most people would think we're probably in that kind of middle section, high gradient piece, just because There's so much happening and there's so many, yeah, so many improvements. There's new models and new techniques and new technologies from academia and the public sector, private sector. Um, and I have no doubt that by the time we finish this conversation, there'll be another technique out there that is worthy of our attention. Um, but my guess is that we're, it's probably more likely that we're at the bottom left hand corner. I don't think we've hit…

AI assessment note: “I think it would be a possibility if, and it's a huge if”

Q Ok. So then, worst case scenario, what, what are we, like, let's say everything doesn't live up to expectations, like, where, when you, when you, you must be game planning this out, like, what do we end up with in the worst case scenario?

A I think the worst case scenario is, there's probably two pieces. One, and this goes back to what we were saying earlier, I think, um, the worst case scenario number one is we've just mismatched where we're at the S, where we're at on the S curve, and we're actually in the top right hand corner. And the capabilities of the, the, the core technology, the models, the ability for the models to be able to work with data at scale, the capabilities to be able to merge those two things responsibly together, you know, That, that they don't mature and improve at the pace that we expect. I think that would be, that would be a disappointing outcome. I think it's pretty low probability at this point, given, given the trajectory that we're on, but that, that could be one. And the other is, again, going back to something we talked about earlier, is that the, the, the readiness of organizations slows down, ah, the, the opportunity to deliver on this technology, um, Because they are struggling to manage the change or they're struggling to, you know, really drive reinvention through some of the sort of, you know, cultural, uh, biases.

AI assessment note: “worst case scenario number one is we've just mismatched where we're at on the S curve”

Q bot, for instance, like people think, all right, I want to make a bot. I go to OpenAI and I build it with them. But what you could do actually in Amazon's technology is go ahead and build an agent or a bot and then pick whether you want OpenAI or Claude, right? It just, it depends. It just, it runs the gamut. That, That's the strategic bet for Amazon.

A That's right, and then our approach is to find areas that are really, really valuable, the customers, real problems the customers are trying to solve, and then add capabilities to bedrock to make those problems smaller. So a good example would be a chatbot. So chatbots, like you may have played with ChatGPT, um, they're very capable. They can understand what you're talking about. They have context. You can go back and forth, and, uh, Uh, and they give the appearance of intelligence, but they actually are not very good today at completing complex tasks. And so let's say you wanted to create, um, a, uh, right retirement plan. You could ask your chat bot, build me a retirement plan. And it would go off and it would build a very kind of reasonable approach to retirement planning.

AI assessment note: “That's right, and then our approach is to find areas that are really, really valuable”

Q already done with, with your technology. Um, I mean, you, you had an announcement today that that's kind of interesting about agents, right? Very interesting. They can build, they can build agents. So I'd like to hear a little bit more about like the practical level of, and maybe you can go step by step of like what, and briefly, but like what people would build with the AWS services.

A Sure. Uh, I think the ones that I've seen that are the most compelling, um, Uh, number one, just generative responses. So the sort of blog posts, advertising copy, you know, three D meshes, those sorts of thing where you're an expert and you just want to, you just want a starting point. Uh, you just want, instead of starting with an empty word document, just give me a first pass and let me iterate on it. Way easier. Huge time saver. You do that all day long. Um, very, very popular. The next area, which is Less sexy, but in my opinion, maybe even be a larger opportunity, uh, is using this technology to improve, uh, search results, improve ranking, relevance, personalization, those sorts of use cases where you don't even know that you're working with a large language model. It's all in the background, but they are remarkable at boosting the, uh, the, uh, the accuracy of those sorts of results. Then you've got, um, knowledge discovery. Uh, so that's the sort of chat bot Example. Uh, and the one that I'm most excited about is, um, collaborative problem solving. So working with, this is a bit more science fiction, but I think we've materially advanced the state of the art this morning with our agents announcement, um, where you are able to, as a, as an individual or another artificially intelligent system, interact with an artificially intelligent service system to solve complex pro…

AI assessment note: “number one, just generative responses. So the sort of blog posts, advertising copy”

Q if you're actually focused on that versus doing these things that generative AI applications can do. So why don't you take us like on a top three interesting things in different industries that you could imagine Generative AI having a real impact, and I think that's, the medical one is interesting. That's a really good one. What else, where are some other examples that we're just not looking at yet?

A Well, number one, I am sure that everything that I'm going to touch on, that there is a startup or even a large organization out there already working on it, and they can, they'll probably be getting ready to ship as we speak. Um, there's just so much investment and activity happening on this area. I think that a couple that spring to mind. Uh, the first one is cybersecurity. Uh, there seems like such an opportunity to employ these language models in the identification of the very subtle signals that have become harder and harder to identify, which indicate some sort of, uh, vulnerability or threat. And so being able to identify those threats across multiple different sources with Better precision is going to be better for everybody. I think that's one. It's not really an industry, but I think it's one that's going to be important. That's good. Still counts. Okay, good. I think another one is just going to be, ah, developer productivity, like code generation. We didn't really talk about that yet. Such a large accelerant. I think there's going to be.

AI assessment note: “the first one is cybersecurity. Uh, there seems like such an opportunity”

Q not quite good enough yet. Like last year, we're going to get to it, but last year we were talking about agents and all these other, you know, advanced use cases and they've clearly not hit. The way that they are supposed to. So what do you think about these limitations? Aren't they the main things that are holding back the field versus just like getting their data in order?

A Well, I think those, ah, there are limitations to the technology today, and part of being successful with the technology is understanding those limitations at a deep level. And you, you referenced, ah, I'm not familiar with the, with the details, but you referenced kind of, 20% of prototypes, you know, going into, into production. Um, honestly, that sounds pretty good to me. Like, if you think of just the amount of experimentation That is happening inside organizations around generative AI. Just the number of experiments that are being run on AWS for different companies in the regulated industries and all the other industries that I mentioned, you know, Bedrock, which is the service that we make available to customers to build generative AI applications. That's one of our fastest growing services ever. And all up, AI machine learning at AWS is already a multi-billion dollar business. Uh, in terms of ARR. So there is a lot happening, and I think that that 20% is actually pretty good because the denominator is absolutely massive, and when technology shifts happen, you really do want customers to be able to, to innovate, to be able to experiment really safely, really quickly with that technology to find out what works and what doesn't work. And we're dealing here with a technology which is, uh, just in its very earliest days, it's much more like a, a discovery than it is an invent…

AI assessment note: “there are limitations to the technology today, and part of being successful with the technology”