Name: Brainstorm AI 2023: The Big Picture: Silicon Valley and AI
Uploaded: 2023-12-12
Duration: 15 min 39 s
Description: Aaron Levie, Co-founder and CEO, Box Moderator: Michal Lev-Ram, FORTUNE

Aaron Levie, Co-founder and CEO, Box Moderator: Michal Lev-Ram, FORTUNE  

Brainstorm AI 2023: The Big Picture: Silicon Valley and AI

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Brainstorm Tech 2023: The Big Picture: Silicon Valley and AI

This next founder has been operating in Silicon Valley for more than half of his life with such experience comes a deep understanding of the power players in the evolving technology landscape at a time when many organizations are calling for efficiencies. Box has already been there and done that in 2021 Aaron Levy successfully defended himself against an investor's attempt to oust him from his CEO position. And as of last week, Fox reported an operating margin of 25% here to talk more about the future of Silicon Valley and regulating A I. Please welcome Box's co founder and Ceo Aaron Levy. Ok. Aaron says it wasn't exactly half. I I think um uh I was trying to do some math. I think it's like one year, maybe less than that. So you should feel very, very young. I I do although I'm feeling very old right now. So I apologize, I don't make a lot of eye contact. Um I have some like allergic face uh issue. Um So we're trying to get to the bottom of it like shampoos and um quite a bit. So I apologize if I'm not like staring right at everybody. But, um, uh, it does make you feel very old although I've been, um, and also I say chat G BT is decent at helping me. I was going to say yes. Are you using it? Well, so the doctors are extremely defensive when I come in and I bring in Chat G BT recommendations. I found that they, it really dramatically changes the course of the conversation and they kind of want me out of there, maybe don't lead with that. That would be my suggestion. Ok. So we're going to talk briefly about Open A I and kind of where I should say the Open A I saga that enthralled all of us. Um One of my favorite uh tweets posts on X from you during that crazy uh few days is what you wrote when everything ended and it was ok. So can we all now agree to never do that again? Um And I'm, I'm curious, I just wanna hear, you know, you're very prolific when it comes to pontificating on a whole slew of topics and, and including what we all witnessed. So what are the, what are the big takeaways for kind of Silicon Valley and, and tech large, I think. Um this is gonna be kind of annoying. So we might need to jump to the next topic quickly. I actually don't do that many takeaways. Um I, I think literally if you, if you rate uh if you kind of did just, you know, looked at the ratio of the amount of drama to the amount of takeaways. It's like a, it's, the ratio is really off. Um, uh, and, um, and I, I like, like, I think the main takeaways don't have weird corporate structures, like it's just like it never ends well. Um, and so that's probably the biggest takeaway. Um, I think if there's maybe another subtlety in this and I'm sure this is what, what's been talked about quite a bit is, is we do have some factions emerging in Silicon Valley around A I. And so I think probably maybe the bigger takeaway would be, um, would be, you know, how do we land the plane as an ecosystem on this topic of A I safety A I regulation and, and these debates and clearly there was, there were real, you know, sort of factions emerging on the board and within organizations, um, more and more on this topic and it's, it's hard to kind of reconcile the two different groups. Um, it's not obvious what, what, uh, what you would land on that would make everybody satisfied because you have one group that, that is, is sort of increasingly believing that A I could end the world. And you have another group that, that sort of believes that's not gonna happen. And, um, and in fact, if anything, we need to accelerate, you know, innovation and progress in A I to get all the benefits of it. Um, and, and those are, you can't square those two things. Um, because one sort of imagines a nuclear winter, I mean, a nuclear, um, uh, you know, bomb going off in the other, um, imagines that, you know, would be able to solve my, my face issue. Um, if we had just better a, I, so like those are like, hard to bring together. I love how you're calling more attention to it. I know, I, I, when no one would have noticed. Ok. Ok. Well done. Um So, but, but, but talk about this because this was gonna be my next question. The two camps, you know, it's not binary, is it? How do you, how do you bring the, the nuance into the? Uh Yeah. No. So the reality is, is that probably 80% of people are somewhere in the middle of that bell curve that, I mean, as with every political, you know, topic. Um, and this is a political topic at the end of the day. Uh You're mostly going to hear about the edges and, um, and, and the reality is, is that, you know, the middle is we, we probably need a lot of progress because there's a lot of benefits to A i, whether it's education or health care or, um, or across the economy. At the same time, we need to ensure that we have a, you know, a decent degree of safety and, and protection and maybe like, like, you know, some asterisks around this, but some degree of regulation, I tend to sort of believe that the regulation should fall more in the category of regulating the use cases of A I across the existing agencies that tend to regulate those, you know, use cases of any technology. As opposed to imagining there's some central, you know, centralized monolithic regulator that perfectly predicts all the things that we should, you know, you know, prevent from A I doing A I from doing. And so that would mean that the FDA should regulate A I in its, you know, area of expertise in the FAA and so on. And that's probably the best outcome because, you know, if you have the centralized regulation of a model, one person might view an output of that model as being dangerous. Another, another might view that as actually being extremely productive and the law really needs to be more about how do you put that output into practice and in, into, you know, some degree of production that's gonna be hard to essentially me to sort of manage. Ok. Um Please raise your hand if you have a question for Erin and we'll get to you a little bit later um on, on, on box, more specifically, um you've been trying to make knowledge workers more effective for many years now, as we heard half your life, almost half your life. That actually is true. It was the silicon Valley thing. We, we, we moved to the Valley about a year into the company. So, so, but, but half my life has been, you know, working with files. Very weird. Yeah. So when I met you at Fry's Electronics in Palo Alto, that was a year and a couple years in. Yeah, exactly. All right. Um R IP prize, by the way. But, um, so, so my question is, um with generative A I, what's different, what's changed over the last year in the product and then how you're engaging with customers and how you're working internally? Yeah. So uh for, for us, I mean, we, we kind of landed on, on, you know what I think of as, as one of the best opportunities in A I because we work in the land of unstructured data and unstructured documents. And so what's inside of a document? It's lots of text. What's, what's a large language model really good at understanding and working with text. So if you imagine like, like where is there a large corpus of information, you know, in any kind of enterprise that's in your documents? And that, that could be your legal documents, your, you know, medical information, um equity research contracts, just anything that you have that is full of text A I models are increasingly good at summarizing synthesizing extracting data from searching across. And so that, that was a really kind of profound opportunity for us. And we tried a bunch of things with A I over the past seven plus years. And what we found is you needed very narrow models for each use case because that was the only way that we could get the right level of quality and efficacy from A I. And so the real breakthrough recently was GPT 3.5 and then GPD four, maybe now, Gemini where you have these models that can work across a large variety of contexts. That's what makes it actually more practical for, you know, kind of any given random enterprise to be able to use the technology. Because previously, when we tried to bring A I to your documents, we would have to say, OK, so you're a law firm that's based in the UK working on these kinds of contracts. Here's a specialized model just for you, that was the only way to really kind of solve these problems. Now, you can do it in a generalized way, which means that you, you actually can scale this as a, as a product. And so we're in the very early stages with box A I, it kind of does exactly what you'd imagine we would do. It takes all of your data plugs in an abstraction layer that connects to open A I or Google and other partners over time and then lets you work across your data with, with those A I models. And you said maybe now Gemini a lot of conversations about vendor lock in and fear of that. How are you guys approaching that question? Yeah, I mean, I think it's prudent probably for everybody to have as much optionality and a future proof strategy with, with your, with your A I, um, you know, certainly if you're a smaller start up, you know, speed the market and thus sort of, you know, probably picking a vendor right now, you know, matters a lot. That was actually why I think the open A I, you know, moment was so dramatic. Um It was not your classic sort of leadership struggle um or dynamic. This was a technology that's been embedded in tens of thousands of products at this point. And so it really had a full ecosystem. Um you know, it was, you know, it was consequential across the ecosystem, which is why you saw so much um uh you know, uh of the ecosystem rallying around the issue. Um But for us, you know, we, we wanna work with multiple vendors because we know there's gonna be different specializations, different costs, um different approaches to the technology. And so we'll plug in um you know, any, any vendor that our customers want to work with. OK. We have a question back there. Hi, Amy Lemire from WXR Funds. So one of our portfolio companies does A I for rashes. Oh my God, thank you. What is it called? Health? I was gonna say any questions just not about his, I'll take any recommendation. If you know an A I app for face rashes, please let me know. Yeah, so they do. But related to that like at what point, how do you assess uh you can trust that data? Like going to the doctor afterwards with the recommendation? So, I mean, actually, I mean, I'm not being facetious. I actually think this is why I'm extremely optimistic about humans and A I and I think this is what gets missed and there's, there's, you know, plenty of good research and literature on this. But, but in my own personal life and I think probably most people's personal lives A I is a rapid accelerant to getting information, getting, you know, insights and expertise and then you almost instantaneously go and ping a person who, you know, as the expert in that, in that domain and you say, hey, does this sound right? So this idea that A I replaces lawyers is just completely impossible. A I will accelerate our ability to get advice that we then go ask a lawyer about. And so because at the end of the day, we're going to need liability, there's going to be liability in the system. You want a person able to apply some degree of human judgment on your specific circumstance and, and the amount of context you have to have about that individual circumstances is at this point, you know, probably 3 to 5 orders of magnitude more than what the A I model is getting when you just do a quick chat back and forth. And so all of that context, all of the situational awareness, all of the understanding of, of, you know, just like what is germane to this particular person's issue because of the, you know, jurisdiction or the place that they're at, like the A I model is never going to really, I mean, anytime soon is not going to have that level of depth. And so, so, you know, whether it's, whether it's coding or recommendation systems or um you know, health care or education, I think it actually increases the ultimate sort of usage of a lot of the surrounding products and services of those industries. So I'd be, I'm mostly, you know, extremely optimistic across the board, even things like designers. Um I, I, you know, my, my, my quick, my quick belief would be that actually the design consumption and thus design services goes up as a result of A I because what it does is it lowers the bar to experimentation dramatically as you experiment and do try new things you instantly say, oh, actually, like let's turn that into a real thing. And then all of a sudden professionals, you know, you go out to some kind of professional for, for whatever that product or service is. So whether it's, you know, again, legal reviews making a web page, health care advice tutoring, I think this is good across the board for the economy. Other questions for Aaron, raise your hand, please. Ok, we go. We've got one back there. Thank you. Hi, Jennifer Fonstad Al Cell. Good to see you. Um My question is about um privacy and around the idea of using proprietary data. So a lot of folks are thinking about um a lot of, a lot of companies are thinking about how to bring in their proprietary data in a way where they're not risking that um the privacy issues around their customer data. And then also thinking about proprietary uh I'm sorry, privacy from uh an application in the cloud such as yours. So I wonder how you're thinking about this and in particular, if there are any vendors that you are finding are useful in those areas? Cool. Yeah, good. Uh Thanks. Um So, so I, I mean, I like, I, I don't wanna turn this into an advertisement but um but unfortunately, like, but I will um and, and, but like your question is exactly dead on um To why, why we think there's a big opportunity is um is uh like, like probably the most boring part of our service um that like literally like I shouldn't even be allowed to mention on stage because it's so boring. Um Access controls and permissions is going to become like a 10 times larger problem with A I because if you just said, hey, you know, here's this, you know, A I system that works across your entire enterprise and accesses everything across your organization. And somebody asks a question of that, the likelihood of that, you know, sharing information that the user actually is not supposed to know about because they're not actually supposed to have the source information accessible to them is very high right now because we've all dealt with, you know, information security through obscurity essentially where we sort of obfuscate the access to the data as the way of securing it. But all of a sudden A I wants to go look at everything, it wants to consume everything and, and so that's going to be this big problem, which is, you know, when Bob asks for Sally's hr you know, data or salary is Bob supposed to get an answer back or not. And a lot of our, you know, typical enterprise systems are not prepared for A I going out and trying to ask that question and getting the answer. So this is where things like again, like just like understanding who has access to what data, the controls around it, the privacy around it matters a ton. Obviously, that's, you know, the business were, but there's gonna be thousands of products that do that. Um On the training side, this is, this is where I think I, I I'm concerned a little bit that, that sort of training your own data set right now is not a complete panacea because um you know, a lot of companies, I think initially want to go and train a model on their data, that, that, but then instantly you ask the question of like, well, what's your like, what data like, like? And then you have to get pretty, pretty narrow, pretty quickly. Um Because anything that is sort of generalized across your business, probably not everybody in the company can get an answer back and, and that would be appropriate. Um And so you can't just like train your company A I model on hr information or on product information because can anybody in the organization ask a question of that A I model and get an answer back? Like would, would that be appropriate? And, you know, all of a sudden you'd have corporate secrets getting revealed. So things like kind of, you know, data protection in the A I, you know, model itself is like a complete, I mean, there's a lot of start ups that I think are working on that. It seems to be like, you know, a relatively either difficult or intractable problem at the moment, unless you put an application layer, you know, between the user and the A I model that is sort of watching for all of that. Um But I would say just super early days, somebody will make a ton of money, um you know, solving that problem. And um but, but these are the things that I think we still have to explore. Erin. Thank you so much. I'm gonna let you go and look at that app. Ok? Thank you. Ok. All right. Thank you, Erin. Appreciate you being here next up. We are going to take a 30 minute break. We'll meet back here at 415 and when we come back, we're going to kick off with a demo from hour one on how to create text into generative A I video content. See you back here shortly. Thank you.