00:19:37 · Ksenia Se, Ulrik Hansen
[00:19:37] S1: So you are their, like, guide, basically, in this scenario? [00:19:41] S0: Yeah. We're the guide, and, like, we, of course, like, build up a product that that helps them, like, bring their models from from prototype into production, like, doing that faster than they would have if they had, like, tried to, like, build a lot of the stuff in house. And a lot of times, like, it's also just not feasible because the complexity of AI systems being developed, I think, is only really going one way. Right? Like, you wanted to solve increasingly complex types of tasks and the problems. And so there is, like, a never ending and evolving need to continue to push the frontiers of your own AI models and systems. So, like, one thing we've seen, for example, is, like, all the models have gone from being unite models to be multimodal. That, like, opens up, like, a whole new set of things that you need to, like, be able to support. This is not, like, attainable for, like, a Fortune 500 enterprise similar to, like, how they move to the cloud because of all the companies that had, like, previously built in house data centers and infrastructure. Now they can, like, rely on, like, a cloud platform, which means that they can, like, move a lot faster on building in house apps and all these other things. I think the same thing will happen in in AI development. [00:20:37] S1: That's very interesting. Everything that we were talking about is on a very practical level. Are there any conversations about AGI with the companies you work with, or what's your personal take on it?
00:20:48 · Ulrik Hansen
[00:20:48] S0: There are, like, of course, like, companies that are working on on AGI. I think my general sense is, like... So we we principally work on, like, more applied AI use cases where they try to actually bring that intelligence into production. At the end of the day, like, treating intelligence as a single construct is difficult because there's, like, different levels of intelligence. Right? There's, like, the cheap types of intelligence, which is just, like, raw knowledge and facts where, like, the models can, like, spit out infinite versions of something at, like, near zero cost. There's, like, pattern recognition on, like, common things, like writing boilerplate code, summarizing documents, and, like, generating standard designs. All that stuff is, like, quite uneasy to do, but it's been a lot of lot of time development. But I think there will be a kind of, like, bifurcation of, like, expensive and cheap intelligence. So, like, expensive types of intelligence are things like human agency, creativity, original insight, taste, and vision, and the stuff that, like, feels like it's, like, genius or original because it can't be reverse engineers from from the data. And so I think the... Kind of going back to the analogy around having AI systems that evolve and, like, kind of, like, doing both of those things, like the ones where, like, we could do it for cheap, there'll be less need for, like, a human loop and fact checking for, like, the ones that... The things that are, like, will be more difficult or require, like, more expensive types of intelligence. Like, that's really where, like, human agency tastes and all these things that you can't really explain necessarily, like, in data will become, like, more important.
00:22:10 · Ksenia Se, Ulrik Hansen
[00:22:10] S1: Yeah. For me, applied AI seems much more exciting than the conversations about something. [00:22:16] S0: At the end of the day, like, I think, like, a lot of the models themselves are, like, good enough. Right? So, like, the main constraint now really is to, like, embed those models and, like, kind of wrapping them into products that can, like, solve real problems. So I think, like, that's where the main constraint is because, like, the form factor, like, matters a lot. Right? Chatability can help you write emails and all these other things, but you can't, like, necessarily have it do your whole, like, job for you. Like, of course, like, these things are getting better, but there'll be, like, a long tail of, like, different types of, like, human tasks and work that, at least, like, for a substantial amount of time, will be, like, outside, like, the rebid of, like, what the the existing form factors that AI is, like, delivered in can support. [00:22:57] S1: In this world where you work with data in all the stages, you know how it works for training, you know how it works in post training, what it needs for the context, working with high stakes companies when it's super important to really manage your data well and be safe about it. What concerns you the most in this world that you help building, and what excites you the most?
00:22:57 · Ksenia Se, Ulrik Hansen
[00:22:57] S1: In this world where you work with data in all the stages, you know how it works for training, you know how it works in post training, what it needs for the context, working with high stakes companies when it's super important to really manage your data well and be safe about it. What concerns you the most in this world that you help building, and what excites you the most? [00:23:19] S0: Yeah. That's a good question. The concerns that, like, people have around, like, AI and, like, making sure that AI is, like, deployed, like, safely and all those things, like, a lot of those concerns, like, are warrant because there are, like, real risks with AI systems. Right? We're seeing that kind of come out in in a few different ways. There's, like, the mass produced, like, misinformation and, like, all those other things, which I think, like, we will just need, like, a good way of, like, verifying whether something is, like, AI generated or has been created by human. But that's also why I think the value of brands will be even more important in the future because, you know, that, like, if a specific brand is, a trust source, like, you go and, like, read the Financial Times or The Economist, I trust that, like, they are as objective as they can be in, like, their assessment of, like, the the truth. [00:24:04] S1: But they have responsibility.
00:24:05 · Ulrik Hansen
[00:24:05] S0: But they have responsibility. Exactly. And so I think that will, like, happen across, like, every single part of the economy. Like, the valid brands will go up a lot because of, like, the general kind of trust and safety issues of of AI. So that is just, like, one thing that I don't think that there's, like, a good solution to other than, like, it makes sense for, like, the existing companies that are doing these things to, like, continue investing in their brand, make sure that they know that they could be, like, the trusted source of things. I think, like, the excitement is is also, on on the flip side by, like, the opportunity that we have and, like, living at this time of history, we're, like, likely to see more change from technology in, like, the next ten years than we've seen in the next... In the last, like, fifty years. And so seeing, like, what comes out of that, I think it's gonna be, like, very exciting. I think, like, the models will start to kind of work in conjunction with humans to come up with, like, new drugs that can, like, treat disease in, like, new different ways that we had never, like, encountered before. A lot of, like, the chores from our daily life will be easier because we will have robots in our house, right, that can, like, do a a lot of the the stuff that you don't necessarily wanna do. Humans will sort of, like... It's a nursing point because, like, we will kind of, like, go back to... We will do... Be doing the things that, like, we find to be most valuable as humans. Right? So it'll be things like spending time with our family and friends and and whatnot. So we're moving from, like, an intelligence economy where, like, value has accrued to intelligence over the last, like, thirty years into, like, more of what I like to call, like, a connection economy where, like, the value that would be delivered... And, like, there'll be new jobs. Right? Like, the new, like, kind of professions created in the agrarian economy a hundred fifty years ago. Was unthinkable that there'd be such a a profession as, a software engineer. And I think we'll see, like, a similar, like, proliferation of, like, new types of of work and, like, new types of jobs that humans will be doing in the future that we can't even, like, conceive of at this point. So, yeah, think it's, like, a very exciting time to be to be alive.
00:25:45 · Ksenia Se
[00:25:45] S1: I'm ready for all of that. And my last question will be about books. I always ask this question. I believe that books shape people, and what is a book or idea that shape your thinking? And it can be related or completely unrelated to AI machine learning.