In short
Big Technology Podcast: Episode Summary
Episode Title
What Cheaper, Faster, and Smarter AI Gets Us — With Aaron Levie
Description
In this episode, Aaron Levie, CEO of Box, discusses the implications of AI advancements following OpenAI's significant price cuts and speed improvements for GPT-4o. The conversation explores AI's impact on jobs, the evolving safety debate, and how companies like Box leverage these technologies.
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Key Themes and Discussions
AI Advancements
- OpenAI's Updates: Overview of the recent improvements in AI technology, specifically focusing on the enhancements made to GPT-4o, which now operates at double the speed and is priced 50% lower than before.
- Expectations vs. Reality: The discrepancy between public expectations for groundbreaking AI developments (like GPT-5 and AGI) and the current state of AI technology.
The Current State of AI
- Incremental Progress: Aaron emphasizes that while recent advancements are impressive, the pace and nature of breakthroughs are often mischaracterized by the public.
- Metrics of Improvement: Discussion on key metrics like context window size, cost reductions, and model quality improvements that indicate significant progress in AI technology.
The Future of AI
- Business Implications: The reduction in the cost of AI models allows for more innovative applications, leading to a greater proliferation of AI tools across various sectors.
- Market Dynamics: As AI becomes cheaper and more accessible, companies must adapt their business models to leverage these changes effectively.
AI and Employment
- Job Transformation: Discussion on the impact of AI on job roles, suggesting that while certain tasks may be automated, the nature of work and job types will evolve rather than disappear.
- Skill Requirements: The need for human workers to adapt to changing job landscapes as AI technology advances.
AI as a Reasoning Engine
- Agentic Behavior in AI: The concept of AI moving beyond simple query-response models to perform complex, multi-step tasks autonomously.
- Real-World Applications: Examples of how AI can serve as an effective reasoning engine in various industries, enhancing productivity and efficiency.
Addressing Concerns
- AI Safety Debate: Ongoing discussions about the ethical implications and potential risks associated with advanced AI technologies.
- Democratization of Knowledge: The potential for AI to provide equitable access to education and expertise, benefiting individuals globally.
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Key Takeaways
- Rapid Innovation: AI technology is advancing quickly, with ongoing improvements expected to unlock new business opportunities.
- Changing Job Landscape: While AI could automate specific tasks, it is likely to create new roles and require different skills, leading to a transformation rather than a reduction in the workforce.
- Exciting Prospects: The current era is seen as an optimal time for innovation in software development, driven by AI advancements.
Conclusion The conversation concludes with a sense of optimism regarding the future of AI and its potential to reshape both the technology landscape and everyday life. Aaron Levie emphasizes that this is just the beginning and that the next few years will be pivotal as AI continues to evolve.
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Additional Resource Links
- [Big Technology Podcast Newsletter](https://www.linkedin.com/newsletters/6901970121829801984/)
- [Big Technology on Substack - 40% Off First Year](https://tinyurl.com/bigtechnology)
- [Contact: bigtechnologypodcast@gmail.com](mailto:bigtechnologypodcast@gmail.com)
Ratings If you enjoyed this episode, please consider rating us five stars in your podcast app! ⭐⭐⭐⭐⭐
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This structured summary captures the key points from the podcast episode with Aaron Levie, providing insights into the current state and future of AI while addressing its implications for businesses and society.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Transcript
Automatic transcript. May contain errors.0:00Box CEO Aaron Levy joins us to discuss where AI goes next after the latest big releases and what building what this technology looks like as it gets faster, cheaper, and smarter. All that coming up right after this. Welcome to Big Technology Podcast, a show for cool-headed, nuanced conversation of the tech world and beyond. We're joined today by BAC CEO Aaron Levy in a conversation recorded live at our first ever public event in front of a packed house in Manhattan last week. It's a really fun back and forth about how far the AI field has come, what companies can build with the technology today and where it's heading as OpenAI and the rest of the pack keep shipping.
0:39Here's our conversation. Well, Aaron, welcome back to the show. Yeah, thank you. So do we just like pretend that we're podcasting or like how does this work? Well, you already have given away the secret, which is that we're back together in a moment of crazy AI news. But this time we're doing it in front of a live audience and in front of the first ever public event for big technology, and we're fully sold out with 130 people here with us in Manhattan. This is going to be the first of many. And listen, the audience isn't going to believe me, so I would love it if you guys could make some noise and let everybody who's listening to the recording know that you're here.
1:17Let's hear you.
1:25There's simply no way we could have faked that. So that had to be authentic and real. It brings us to sort of the topic of our discussion, which is the latest in AI. And of course, there's everything from synthetic images, synthetic text, synthetic voice, synthetic video, which we heard a little bit about from Google recently. But let me give you my big picture here now that the dust has settled a little bit from Google I.O. My perspective is that we haven't exactly seen the groundbreaking stuff that's been promised. right we we were looking at gpt5 and ai sentience but we haven't gotten that yet so am i right in thinking that yes we've had some you're very impatient if that's uh if that's your if that's your issue well i was i'm setting you up to own me at the beginning of this discussion okay uh but i do want to know like should is there something that we should be thinking about in terms of what's going on right now that doesn't look at what we just saw over the past few weeks which Yes, they were impressive, but they're not this sort of promised godlike AI that everyone keeps talking about and waiting for this GPT-5 moment.
2:30So you were unimpressed by GPT-4-0? I wouldn't say I was unimpressed, but I would say given that... Did you see that video where the guy was doing a job interview and then her little thing was explaining how he should look different? I mean, this is psychotic technology. I mean, this is incredible. I don't deny it. But I also think you have to understand where I'm coming from, which is that a lot of people, when they saw, well, actually, that's now the past. But a lot of people, when they saw this stuff, were just like, reasonably or not, and I'm just trying to channel the audience. Where's this audience, the audience on X, which we know is the most reliable audience on tech?
3:09The X people are crazy. Yeah, well, we're going to read some of your posts, so let's see who's crazy at the end of this. How do you think I know how crazy they are? That is a good point. So, okay. So, but I guess, where would you say we are right now? And why haven't we had that sort of step change? Because this is the thing. We talk about the GPTs and it very quickly becomes old. Like GPT-4 is pretty impressive, but it already feels like old news and people want something new and they want to feel like that aha moment that ChatGPT brought, and we haven't had that yet. Now they're impressive, but like - I just think these people are like, sound like heroin addicts.
3:42Like we just need another, I just need another breakthrough. Well, they do call tech people users. and that sort of fits. It comes with the territory. Yeah, so I mean, I basically don't agree with the premise. So I think that this is the craziest technology ever and it's entirely reasonable that we will see this in sort of step change in this kind of step change fashion. So if you think about it, we're like 18 months into the initial Chachapiti moment. We have seen breakthrough after breakthrough through in the past 18 months on AI model performance, on AI model effectively intelligence, when you look at the evals that these AI models are put up against.
4:26When you think about one interesting metric is context window, which is the amount of basically data I can put into the AI model or get back from the AI model. And at the start of Chachabut in, let's call it November of 2022, the context window was somewhere We're on the order of about 4 ,000 tokens. Just yesterday, Sundar announced 2 million tokens on the latest Gemini model. So when you think about it, there's like not that many technologies literally in the world that see an improvement at the rate of 500x in 18 months. And that's basically what we're seeing in AI. So that's obviously one metric of performance improvement.
5:06But you kind of look across the board, whether it's the cost of tokens dropping, whether it's the improvement rate that we're seeing. All of this is building the foundation for now downstream, I think, an incredible amount of innovation. And then you look at just literally on Monday, the GPT-4.0 Omni model is also another breakthrough just in terms of, you know, obviously you always have to kind of look at these demos and say, okay, how much of that was really just like the perfect demo and they kind of knew it would work well in that situation. I think we can chalk up some of the use cases to that.
5:40But I'd say ChatGPT and OpenAI generally have been incredibly intellectually honest stewards in this space. And so when you see a demo from them, it tends to actually work like that in practice. So that ability to have multimodal experiences where you have video in and audio out or video in and text out in basically real time because it's in the same model. I mean, this is going to produce some pretty incredible just both personal experiences and then not even touching on what's possible in the enterprise. So I think, first of all, I think we should actually probably be glad that we don't have this sort of breakthrough AGI experience yet because we actually need some time to sort of just pace ourselves, frankly, in the deployment of this.
6:24I sent a video of the sort of interview example, and if you haven't seen this, please watch it. This person sort of takes a video of themselves in a real time. They're talking to the GPT-40 model saying, you know do I look presentable for this interview and it's giving it feedback I send that to my parents and basically they're like well like we basically don't need humans anymore and um and so like like to somebody like that like this is AGI like we could stop right here and probably be done with AI for like a decade and you've already you've already solved like hundreds of use cases that are these breakthrough you know kind of situations they have another video of somebody um uh who basically you know uses um AI to see the world uh they're they're they're basically blind and they see the world and they're able to now communicate with AI to have so much more capability than they would have had before.
7:15So, I mean, these are just incredible technologies, just even in the current form, let alone when we actually get access to GPT-5 and so on. And the place I thought you were going to go first was really the cost, because that's the thing that you've really harped on over the past few days. I'm trying to anticipate your answer. I'm like, all right, he's definitely bringing up the cost thing. And I think it's worth spending a minute on it, which is that OpenAI has made the cost of GPT-40 50 % of what it was previously. And for you, you're working with a lot of AI. I mean, just talk a little bit about what that does for the industry, for anyone who's building on top of this stuff in terms of its ability to be a profitable investment and to be something that is more ubiquitous than it is now.
7:56Yeah, so there's probably, I mean, so there's basically three dimensions that AI is going to need to improve on before you even worry about, you know, kind of agentic-like experiences. So even, you know, kind of bookmark that. You have effectively the, let's say, you know, more or less the quality of the models. So how do these models perform on evals? and these evals are, you know, basically throw a bunch of problems at AI models and then you get a sort of a shared benchmark across how different models perform. So, you know, LAMA 3 versus GPT-4 versus Gemini and you get to see sort of how it does against the LSATs or MBA, you know, courses and so on.
8:38So one is sort of model quality. We've already seen that the latest GPT-4 models and kind of of the GPT-4 class, in many respects perform better than humans at a large number of tasks, but in some areas are still deficient and not yet at kind of human-level performance. So model quality is sort of one vector that we need to continue to get performance on. And you can imagine, you can just extrapolate and imagine GPT-6, let's say, is like we're like at 99 % human level and then GPT-7 is like 99.9 and then GPT-8 is 99.999. nine. So, so we'll see some, you know, some type of, of asymptoting, but, but eventually you're just going to continue to get better and better model quality.
9:20That's, that's sort of, you know, vector one. Vector two is how much data can I put in the model? And this has previously been something that was very limited, actually a large number, a large reason why, frankly, I think so many of us were, were not yet sort of figuring out how big of a breakthrough this was, was back in, you know, the GPT-2 days, you know, the base, like all you could give it was like a couple hundred, effectively characters or tokens. And that was basically all you were working with. So it was very hard to kind of imagine sort of next token prediction when you can only give it a limited amount of data and context.
9:53And now we sort of have breakthroughs now with, again, 2 million token context windows. That's a massive breakthrough. So number one is quality. Number two is how much data can I put in the model? And number three is cost. And sort of then the performance of the model. Actually, I should probably add just one more, which is speed. but with cost and speed kind of come in a little bit of the same dimension. So when you have what you saw on Monday, GPT-4O drops by 50%, literally, you can just think about it as, I just took the ability to have intellectual capacity, and one day it cost X amount, and now it costs 0.5X, like overnight.
10:33That's pretty crazy when you think about that. And the fact that we've now done that, or not we, I mean OpenAI has basically done that, you know, I don't know, four or five times in the past 18 months. So we're already maybe a 10th of the total amount of cost of a kind of fairly high quality token just in the past year and a half since the kind of first version of ChatGPT. So this is a breakthrough because if you have a use case with AI a year and a half ago, that may have been slow. You may have had to hack around it and it may have been relatively expensive. And a year and a half later, it's much cheaper.
11:07you don't have to hack around it because you can give the model a lot of data and it's much more intelligent so you just it doesn't take that much imagination to say well in 18 months from now what am I going to be able to then create and build and so you just sort of watch these curves and and I mean the implications I think are going to be massive for startups mostly positive a couple kind of question marks which is if you're watching this curve you probably should be building software for what is going to exist in three years from now as opposed to today because you don't want to be building software that sort of assumes that the tokens are expensive and they're not that high quality and they're kind of slow.
11:42So we spent a lot of time thinking about like, are we designing a system that is sort of, you know, kind of just covering up some of the shortcomings of AI right now, or should we design a system that will work really, really well in a year and a half from now as we get more of these improvements? And that's, you know, the ongoing battle, I think, of anybody building AI, you know, startups is, you know, do you build for what you have today? Do you build for what might exist in the future? How do you avoid getting disrupted from the model? Just sort of kind of basically building in your value proposition directly in the model itself.
12:11So many questions, but ultimately I think, one of the most, if not the most exciting time in history to be building software. And I definitely want to get back to this idea of what we should be building or what software companies and startups should be building. But as we talk about the cost of intelligence coming down, it does make me think like, is there a viable business model for all these companies that are spending hundreds of billions or tens of billions of dollars training models and then selling this intelligence at lower and lower rates. And one of the data points that I think about here is OpenAI, in the middle of this whole Sam Altman thing, when he was fired and then brought back in, they were in the middle of a reported fundraise that was going to put them at, what,$100 billion?
12:54And we haven't heard anything about that yet. So maybe that's a little bit because of the, they wanted to make sure the board was settled. But what are the economics for these companies? And is this really sustainable for them to keep providing this for less and less cost? I mean, a 50 % cost is a big deal. Yeah, yeah. Well, you know, what ultimately matters is what is their cost. And then, and so one would theorize that they have come up with algorithm improvements and model improvements where their underlying costs of running the tokens through have now dropped by, let's say, 50 % or whatever.
13:30Obviously, it's sort of hard to pin down because there's no public information on their gross margins. But in general, I'm guessing that they've done something that has driven efficiency that has made their cost structure lower. And so then they're basically kind of giving us that cost structure improvement as customers. So then their theory is, well, if we drop the price by 50%, do you basically get more than a 2x in usage and volume. And I would argue that basically at every point in AI performance improvement, that sort of trade has basically come true, which is if you could make, again, kind of wave a magic wand and you say like we have GPT-5 or GPT-6 and it costs like a tenth of what today GPT-4 costs, I would argue that you'll probably get 100x more usage of AI, not just 10x more usage of AI.
14:19And so at some point, maybe that plateaus, but we were like nowhere near the point where a lowering of cost doesn't sort of disproportionately impact what you can now build, which then impacts more volume. This is actually an interesting thing. If you looked at, I have no kind of interesting anecdotes on this. You should probably do the research. But in the very early days of cloud computing, everybody looked at the size of the server market and they basically said, well, if these servers are in the cloud, we should kind of take the size of the server market and maybe shave off some of that spend because as it goes to the cloud, it gets more efficient because you have shared capacity.
14:55So you get less underutilized capacity. And a lot of the kind of total addressable market analysis of cloud computing was looking at the historical usage level of on-prem data centers. And that's totally fair because that's kind of like all you could really do if you're doing that analysis. But what they didn't realize was as you created sort of on-demand computing resources, it meant that literally every developer on the planet could now actually have access to servers. And you could just like start a company tomorrow and then use computing capacity, which you didn't do when you were a startup, you know, 25 years ago, because you just like couldn't, you know, put servers in a data center.
15:32So like you just didn't start the company in the first place. So all of a sudden, the cloud computing scale was like 10 times larger than what you used to do in data centers. And so, you know, similarly, as you get the cost drops of either the GPUs themselves get cheaper or model efficiency gets better, you'll see just a massive increase in utilization. So I think the business model is still very good for the top, let's say five or so model providers where you will run into a question is, could you be the 15th LLM training company? That seems tough, especially if your job is to do kind of horizontal LLMs.
16:08I think that'd be less likely to work. You also have this battle of like something like a Mistral, which was for a period and maybe still ongoing, was like a massive breakthrough in open source AI. And then Meta one day decides to just exceed all the benchmarks with Llama 3. So I think there will be some parts of the market where there's going to be a lot of competition, and it'll be hard to kind of figure out what the business model looks like. But in general, I think the business model of providing high quality, cheap, highly scalable tokens, if you're anywhere in the top three for quite some time is going to be fine.
16:47And then ultimately, if you're a hyperscaler in the cloud, what you really want is all of our workloads. You just want us to build our full application on your tech stack. So you're not really trying to make that much margin on the AI itself. You actually want the data, you want the compute, you want the storage. So I think the business models will all continue to be fine to continue to give away this technology at a lower and lower price. Okay, and so I started our discussion talking about how disappointing this release was. I mean, let's actually talk about the impressive stuff, right? One of the things that I saw over the past at these events within OpenAI and Google was that these models have an ability to reason, right?
17:23They seem to be able to take problems and then break them down to their component parts and then go through step by step. It's not like the traditional ask a question to an LLM and it will give you an answer. It looks actually like something that's smarter. So did you pick up on that reasoning capabilities? because we had this whole like moment in the Sam Altman thing where like that people talked about this QSTAR model which could reason and did math and I watched some of these demos and I'm like is that it? Yeah so I think you know things like reasoning were still very early on. Anybody now you know deep in the AI space you're going to hear us all talk about this idea of agents and and kind of what is this agent like behavior or agentic behavior that you can have in AI models which really moves from going to an AI model, asking a question, and then just basically getting the text output or audio output of what that model is producing to actually giving it a problem that is often multi-step in nature, maybe interacting with other systems, i.e.
18:20other tools, and how do you kind of put that all together where a single AI model connected to these tools can actually produce effectively an agent that really can actually execute full tasks and processes. So we saw maybe slight examples from that both on Monday in the OpenAI announcement and then yesterday in the Google announcements. I'd say both at a very high level, just because actually so much of this space is still at a pretty high level. If you go and ask like 10 AI startups that are doing anything with agents, you'll probably get more or less 10 different architectures of kind of how the agent actually functions.
18:58but what is at least similar to all of them is the LLM or the model is really acting as the reasoning engine and basically the brain for, you know, kind of coordinating and executing tasks across other systems and software, which is a very exciting concept because, again, a year and a half ago, I think what we thought, you know, at least we internally thought and what we saw from startups was, you know, this is like this, It's like a chatbot wave. And the chatbot was really just like the best, you know, kind of way to manifest AI to get people to see the power of it. But, you know, the chatbot is just like one of, you know, a thousand modalities that we might have with AI.
19:39When you start to think about the AI is not something that you just, you know, chat back and forth with, but instead it's sort of a reasoning engine for anything that you want software to do. It opens up a very different world of possibilities. And what about the personalities of these bots that we're going to see? I mean, after OpenAI did its release event, Sam Altman tweeted out her. The bot was just extremely flirty. And then it didn't work the next day. And I looked at it and I was like, oh, if they were trying to build an AI girlfriend, they nailed it. Super flirty in the first interaction.
20:11Doesn't answer your text the next day. Aaron, what do you think about the... Do I have to answer that question? Yes, you do. What do you think about their attempt to build her? I mean, I do not know the internal kind of workings of how did the voice get kind of tuned to be the most interactive and engaging voice. That's a fun way to describe flirting, but yes. Engaging as a euphemism. But I mean, I thought about that for about 3.2 seconds when I saw it. And obviously it'll be like a big controversy online. But the market will effectively decide what voice we want from these things. And I expect OpenAI, Google, et cetera, to kind of land on what's the right equilibrium of kind of, okay, a little bit too creepy versus way too robotic and utilitarian.
21:05And so somewhere in there is probably the sweet spot. And I think we'll kind of go and do a little bit of pendulum swinging until we find that. And you spoke about this in the beginning, and I don't want to gloss over it. this capability for the AI to be a tutor. Yeah. Right? I want you to kind of unpack how important this is because this can really change the equation for parents where everyone has this idea, okay, set the kid with the laptop. But if you can set the kid with the actual tutor that's going to work with them, personalized through the notes. And not only that, but Google had this example.
21:40It can listen in on a PTA meeting for you and take the notes there and tell you what happened. yeah it's almost like taking ai and putting parenting on autopilot and everyone's going to be like that's weird and creepy but it also is like in the best cases this technology gives us more time to do the human stuff yeah right and if you have more time to actually be a parent to your kid like be caring with them be present with them yeah as opposed to having to go through this work with them i think that's a could be a pretty special thing yeah i i don't know like statistics on parenting globally on tutoring and like how many you know parents are good tutors but Not many.
22:13Okay, well, so let's just assume that a significant portion of kids do not grow up with the highest quality tutor access. I got lucky because my dad was sort of into that and my mom as well. But let's just say that's not the case everywhere. So obviously, if you could make AI freely available globally that was as smart of a human and you had the interaction paradigm work where I can just interact with it to learn, that is like only a good thing for humanity. Like it would be literally impossible to say, I want to shut that down or I don't want that to exist. We can talk about all the implications of, okay, how do you make sure that it's as available as possible and bias and all these other things.
22:55But like the idea that we could basically democratize access to knowledge and tutoring and help and education to everybody on the planet is basically a good thing. And that's just like one of the many examples of, I think, the power of AI, especially in the consumer side. You know, take that for healthcare. Take that for basically any kind of subject that I want to be educated on. Take that for just learning how to code. I mean, the amount of, you know, sort of the easing of the on-ramp of what we as people, you know, spend a lot of time learning and let us just explore more spaces to figure out what are the areas and domains that we want to go really deep in.
23:34That is just a very good thing for the world. Okay. It's become a tradition on Big Technology Podcast to do a segment where we read Aaron his tweets and make him explain them. So let's do that now. We can also stop that today. That's not something that has to continue. You know, let's continue it. So let's see. The problem with reading tweets is like, it never sounds like when I wrote it. So it's just like, it's a totally different time in the day. It's like a different voice. But go for it. I just don't know. I mean, you're going to read something. everyone's gonna be like that's not that insightful and then i'm gonna be embarrassed and then um and then i'll have to explain myself but go for it no no i i handpicked them because i think there's gonna they're gonna start good discussions okay and if this sucks i'll retire the segment i promise okay okay okay okay uh so this kind of goes to the agent thing a large portion of business problems are constrained uh by how much time any given problem takes to solve and the number of people you have to solve it ai flips this by creating a world where we can solve problems by essentially throwing more compute at them.
24:37Yeah. Do we just? Yeah, you riff on that. Oh, riff on that? Yeah. Just riff on my tweet? Yeah. Okay. It's in the tweet. So, I mean, the only riff I could add is that, you know, there's this, the only reason I wrote that was just classically in business, you sort of have this term of like, let's throw more bodies at the problem. And obviously that just means like just how much headcount do you have? Like we'll throw more bodies at this engineering problem, at the sales problem, at this, you know, whatever the thing is. And it's kind of crazy to think about a world where you would just say, let's throw more compute at the problem.
25:16And the equation goes from, okay, I got to call the HR team. I got to make sure we have budget. We have to go hire a lot of people. To now it's like, well, do you want like 100 leads or 1 ,000 leads or 10 ,000 leads? And that's not going to be driven by how many people I hire. it's going to be driven by how much compute I have. Do I want to, you know, test, you know, 90 % of my software for bugs or 95 % of my software bugs or a hundred percent of my software for bugs. It's not, again, how many, let's say test cases I write or quality engineers I hire. It's how much compute I throw. So you can kind of, you know, work through like how much of business now can become a problem, uh, uh, where, where we can throw compute at the problem to basically solve it.
25:57And it's just like a different way to think about, you know, organizing your company, how you scale your company, and ultimately, you know, the role of kind of intellectual labor instead of a business. Okay. Yeah. This is a good segment. Okay. Okay. Got it. Okay. Okay. You're not biased in any way, but perfect. Okay. Okay. I did come up with a segment. The cool thing about this is another one. The cool thing about AI is that Zuck is unleashed. There's a brand new platform opportunity. The market is early and up for grabs, and there will be multiple winners, and it uniquely leverages the strengths of meta, the breakthroughs in this space will continue to be wild great tweet yeah just riff again so uh i mean that would so i'm just supposed to describe that like why why meta in particular is positioned and what is zuck unleashed zuck unleashed is just i mean did you see his birthday you know photos i mean he's the guy's unleashed um uh so uh so you know you have so i don't know if anybody was around in like the mid to late 2000s doing web stuff, but you had this conference called F8 and sort of Facebook was at the center of basically, you know, web software in sort of the consumer world where they created the social graph.
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27:16You built your application kind of using their APIs. That was sort of like, you know, like they were really, you know, driving the web forward. and unfortunately I think mobile probably sort of slowed that down a bit because the conversation then really flipped to iOS and mobile platforms and so you know you had all of this energy and technical talent from meta that was kind of underutilized in a mobile world and they really couldn't they didn't have any platforms so so that is sort of where the metaverse came from and uh and where Oculus came from was I think you know Zuck's entrepreneurial spirit on like well, let's build a platform that we own and that everybody can build in.
27:57And I think just the reality is that at the scale probably that they would have wanted, we're not all in the metaverse yet. So you basically have this incredible entrepreneur with insane resources, both in engineering and CapEx, that has kind of been a little bit held back because he hasn't had a platform to be able to unleash into the world. And now there's this spot that's open, which is open source AI. is not owned by anybody. And so he's got all of the right resources for it. He's got basically the entire industry rooting for it to work because we all benefit. The cheaper he can make AI models and the better he can make AI models, we all win.
28:38Because either that will mean that OpenAI and Google will want to work even harder and lower their costs, or we just literally have an open source AI model that we then don't pay any kind of fees for, which is incredible, other than the cost of the GPU. So he's got all of this pent-up energy, this is just me imagining how he's thinking about it, and you watch his videos, and you can see he knows he's on to something, which is he can win in this open-source AI world, which is going to be a very, very big space to be a part of. And then commercially, I think it's always good if your direct competitors do not win a large portion of what the zeitgeist is talking about and focused on.
29:17So it's good if he has some way to defend against, let's say, how big Google gets or open AI gets in this world. And then on the offense side, he can probably just make more money if he has people spending more time on his platform and asking questions and getting recommendations for things to buy. And all of that will be powered by AI in the future. So I think it's both going to be a commercial success. And I think it's like structurally, strategically something that is going to look like a very good decision in the long run. All right. Let's go to the other member of the cage match, Elon. Okay.
29:53You said, finally got around to trying the latest Tesla full self-driving last night. Can't confirm it's wild. Yeah. Is it actually, does it feel like real autonomous driving or were you still, is there still fear for people's life when they're in there? Well, those could be the same thing. So it might be that real autonomous driving, you still fear for everybody's life because you're just like, I do not know how this works. Like, this is kind of alchemy. This is crazy. But it was it was definitely very crazy. It was a very, you know, kind of relatively boring suburban kind of trip. But but it was like there was just zero need to ever, ever interject.
30:32I mean, you have to kind of show that you're still paying attention. But but I mean, it just it shows again, like the past year and certainly for the next couple of years, you get the sense that we're going to see hundreds of these these like like early previews about the future um which is just pretty exciting like uh i mean i've just never seen a period where you know in any given week you could see two to three things which are just like obviously that's going to be the future maybe it doesn't work perfectly right now but but it's like there's nothing that is is stopping it from working perfectly in a world of more compute and and just more breakthroughs on on the models themselves.
31:12And that is kind of where we're at right now. It's pretty cool. It's really cool. It'd be nice if that happens because obviously we have way too many traffic deaths. All right. Cutting room floor, I won't ask you to react to these, but just for time. But you have VCs when.ai is in the name and there's a bunch of people doing some like parkour off of buildings. And then... So you're going to verbally explain visual tweets. This is... This is good podcasting, man. That is aggressive. That is aggressive. Oh my God. And then there's... I'm looking at screenshots of videos right now. So this is, I don't know.
31:43This is actually a screenshot of, this is a, Oh, okay. This is a, everybody wants small government until there's something they want to ban. And it's a Ron DeSantis standing in front of a table of lab grow meat. Yes. Also, do I need a riff? You get that one. What is that? You can give it 60 seconds on that. Okay. No, no. I mean, it was just, I mean, I mean, the, I think that was also kind of pretty straightforward without, you know, conveying my political views. I just found it ironic that the, you know, party of like, we want the smallest government and, you know, more libertarian oriented values is, you know, not going to let, you know, science breakthroughs happen in their state.
32:22So it's like, okay, well, maybe actually, maybe it's just only when it's convenient do you want, you know, small government as opposed to this is a very principled, you know, kind of decision. And so that was literally all that was referencing. So yeah, no, I enjoyed that one a lot. Okay. We're here with Aaron Levy, he's the CEO of Box. We're recording live in front of an audience in New York City, our first public event ever. We are going to take a quick break and come back with audience questions. So we'll be back right after this. Did you know your credit card points and miles can lose value to inflation?
32:54Credit card companies often reduce the redemption value of your points and miles. Now, imagine a credit card with rewards that can grow in value. With the Gemini credit card, you can earn Bitcoin or one of over 50 other cryptos instantly with no annual fee. Every swipe at the store or gas pump earns you instant rewards deposited straight to your account. Plus, sign up now for a$200 Bitcoin bonus to kickstart your rewards. Visit Gemini.com slash card today. Check out the link in the description for more information on rates. Again, if you're looking to invest in Bitcoin but don't know where to start, the Gemini credit card makes it easy.
33:32The Gemini credit card is issued by WebBank. In order to qualify for the$200 crypto intro bonus, you must spend$3 ,000 in your first 90 days. Some exclusions apply to instant rewards in which rewards are deposited when the transaction posts. This content is not investment advice and trading crypto involves risk. The Gemini credit card cannot be used to make gambling-related purchases. What the hell is going on right now? And why is it happening like this? At Wired, we're obsessed with getting to the bottom of those questions on a daily basis. And maybe you are too. I'm Katie Drummond, the Global Editorial Director of Wired.
34:09And I'm hosting our new podcast series, The Big Interview. Each week, I'll sit down with some of the most interesting, provocative, and influential people who are shaping our right now. Big interview conversations are fun. I want a shark that... That eats the internet. That turns it all off. Unfiltered and unafraid. So in a lot of ways, I try to be an antidote to the unimaginable faucet of reactionary content that you see online, to the best of my ability. Every week, we're going to offer you the ultimate luxury of our times. Meaning and context. True or false, you, Brian Johnson, the man sitting across from me, one day, at some point, as of yet undefined in the future, you will die.
34:53False. Tell me more. Listen to The Big Interview right now in the same place you find Wired's Uncanny Valley podcast. Subscribe or follow wherever you get your podcasts. And we're back here on Big Technology Podcast with Aaron Levy, the CEO of Box. We're in front of a live audience here. Yeah. In New York. Okay, cool. He remains here. He hasn't left despite the reading of the tweets and the conversation about lab-grown meat and the live ad. So let's see if we can keep going with this. So what we're going to do now is we're going to take some questions from the audience and hopefully we'll be able to record them and get them on the podcast.
35:30So let's do that. If you have a question, raise your hand. I'll come over to you. State your name and where you're from. And I have a plan. I planted a question in the beginning with Ranjan Roy. He's one of our favorites on Big Technology Podcast. He's our everybody. People who are listening to the show, you guys know Ranjan. Let's give it up for Ronjan. This guy is amazing. He's on with us every Friday, and I think he has some questions. I was very interested in the whole idea of agentic experiences. Are there any current ones that you already use in life on a day-to-day basis that you've either hacked together or created, or what are ones that you think are the most exciting in the near term?
36:17Cool, okay. So on agent experiences, I don't think I have any that would qualify as agentic in my life right now, partly because these things are just so new. And I actually think most of the use cases will be on the enterprise side. So they're going to be things that we never even see that are just happening behind the scenes in our technology and our software kind of every day. there's I'd say I don't know how this exactly happened but some something like six months ago there must have been a memo that like everybody read that sort of set off this kind of agent kind of startup wave because in the past month or two I've sort of increasingly seen new startups that all have a somewhat similar pattern that is basically defined by or defined as, you know, traditionally when we, you know, when any of us sell software, we kind of say, hey, like, you know, your employee X, you know, does a particular business process and here's software to let them like go and do X.
37:27And like, we're going to enable them to do that thing better. And all these new agent startups are kind of saying, hey, you have a task that somebody does or maybe you never got around to, so it's not even like you don't even have anybody doing it. We have software that will do that task for you. QA a website, do outbound sales, generate a marketing translation. And this pattern is emerging pretty rapidly from what I can tell where I've seen, I don't know, maybe a dozen or two dozen startups like this, but it feels kind of akin to, honestly like the early 2010s almost where we finally figured out what mobile was going to look like.
38:13There was a few years of pretty shoddy kind of approaches to the mobile wave like 07, 08, 09. You're kind of like, that's kind of like janky and there was a web app probably and it didn't work really well. And then all of a sudden it was just like boom, Instagram, boom, Uber, boom, boom, Instacart, boom, DoorDash. And we were just like, oh, actually, so your phone is this sort of new command center for just like things. And then like everybody got the memo and then we were off to the races and lots of startups didn't work, but like we at least all kind of knew more or less how this was going to work.
38:46I think we're now emerging in the space in AI where we're kind of getting that memo, which is like, no, it's not going to be like 150 different chat applications. Maybe there'll be a couple that kind of make it. But it's actually using AI as more of a brain behind the scenes for really kind of just taking work that you would have done otherwise and automating that. And that's pretty exciting. I think the one thing that is really interesting about it is that it does really put a lot of pressure on how you architect whatever it is you're building. So I have a friend working on a startup and we'll go through what he's building.
39:28And at any given day, the updates to a GPT-4 or Gemini are just like basically solving entire components of what you would have had to go and sort of mask or make up for if you were building a GPT-3.5 paradigm. So like pretty wild that just from 3.5 to 4, you do a lot less sort of, you know, kind of constraining the system and preventing it from doing things because now you can take advantage of more of that AI model. And so it's almost like actually like shit, like maybe should you be building a startup only just anticipating GPT-5 and don't even worry about GPT-4? Like it kind of almost begs the question of like, don't launch anything right now.
40:07Wait till this thing is even more intelligent. But of course, at some point, you could do that. If you extrapolate that out too much, you just wouldn't launch anything. So it's hard to know exactly the moment to launch. But it does really mean that you need to future-proof your architecture in a world of agents. All right. I'm going to go there, and then I'll come here. Hi. Hello. My name is Ilya. Actually, ex-Enderver staff and current co-founder of Morphosis. So I wanted to ask, we mentioned at the beginning, like... Did you say your name is Ilya? Ilya. Ilya? Yes. Like the most famous name in AI?
40:40Okay, wow. Well, this is she. Okay, yes, different, but yeah, okay. So actually in this current AI and of course like future AI world we're living in, do we need humans or what do humans need? Well, we have to build the AI. Yeah, of course, but what skills do humans need in this current evolving AI world? Yeah, I mean, A, great question. the question, obviously, for all of us. I think my answer will be pretty unsatisfying because I think, honestly, we don't know the answer yet. Sorry, let me actually say yes, we need humans. What we should go do about that, I don't know yet. I don't think anybody really knows because, again, the pace of sort of AI development is happening so quickly.
41:28But I am not convinced yet, and I've spent hours debating everybody I can on this. I'm not convinced that AI doesn't look fairly similar to prior technological revolutions. It feels like it's different this time because it feels like, well, the intellectual thing is now coming after intellectual stuff. But I'm not convinced that it actually, in a grand scheme, at a macro level, looks any different. in the sense that what I expect to happen is the tasks that we do every single day will just begin to look very different. And it'll look like a little bit different at first and then a little bit different thereafter.
42:12And then you zoom out and 10 years from now, it'll look totally different. So it almost won't even necessarily, like we won't even feel it probably because it'll just be these incremental changes that amount to a large amount of change. But if you like, Like, you know, if I showed what I do on a computer screen to my, you know, to, you know, previously my grandparents, they would be like, what are, how are you creating value in the world? Like, you're just on a computer screen and you're just sending an email to like back and forth. And then you're like in this Slack thing, just chatting. And it's like that, that creates value in the universe.
42:48Like, it would just be confusing. Right. Because they'd be like, well, why are you not in a room with a chalkboard and talking about a thing and building a... So imagine in 20 years from now, the version of that, which is the person doing work, it just says, hey, I need you to quickly analyze this market and all the trends on it and come back with an answer about this thing. And five seconds later, it comes back with that thing. like that would obviously have compressed, you know, let's say 20 hours of what a human would have done. That doesn't mean that all of a sudden we're going to not work those 20 hours.
43:24It just means that we have the answer to then move to the next step in whatever that particular process is, just 20 hours, you know, sort of sooner. And I think if you just kind of multiply that out against kind of all of our work, I'm not convinced it then sort of meaningfully changes the job equation. Now, of course, this is one of these things which is like, it would be really bad to look really wrong on this, so maybe this podcast will be the end of me in 10 years. This is not our goal. Yeah, exactly. I thought the tweet reading was the problem, but it was actually predicting that jobs are fine.
43:59And we're totally fucked. But I think that in any area where we can bring automation, for the most part, doesn't mean the job won't change or shift a little bit, but But for the most part, you generally just get either more jobs or a shift of what the labor was doing as a result of that automation. And my thought experiment is, and again, the whole system is sort of experiencing this. Maybe there's some sort of unforeseen factors, but again, I'm still pretty convinced of it. My general thought experiment is like a very kind of simple one. If I could get an engineer within Box to write 20 % more code, and let's just imagine it's all perfect code, or a sales rep to be 20 % more productive, i.e.
44:43for the same dollars, they can sell 20 % more in revenue. In both of those examples, the improvement gains that we see, I'm going to reinvest those gains back into the business to grow even faster. In neither of those cases, am I as CEO or my co-founder or CFO, where we're going to take those dollars and just be like happy with higher profit levels. Because firstly, because we're gonna be competing with somebody who will use that productivity gain to compete even better. So it's not like any of us are in a static market. So we will just have to go and reinvest whatever that performance improvement is back into the business, which would mean more sales reps.
45:21Because if right now you're paying them X and they can generate X times three, and now they can generate X times 3.5, like I want as many of them as I can humanly get, probably up to the point, frankly, where it goes back down to three. And just because there's sort of a natural rate that you expect kind of a sales person to be productive at. So I think that's going to happen for most jobs. Again, there'll be nuances. So if today you're doing like very frontline customer support where the customer emails and they say, hey, I need to reset my password. And the AI now does that. What does that mean?
45:55My hunch still is that actually you'll just move to a higher level set of tasks that the customer is asking for. But maybe some of those jobs have to shift into more customer success as opposed to customer support. So in anybody who does B2B software, we can't get enough people to spend time with our customers. Like it's just like there's just a cost equation. Like I would like to have more people that can go spend time with our customers. Instead, we have to spend a certain amount of time and dollars on just pure inbound like I have to change my password type email. So I would take those dollars and reinvest them into things like customer success.
46:27It would actually be the same person. Like there's like it's like the skill is relatively transferable, it would just be a different set of work that they'd be doing as a result of what we freed up. Again, there will be examples that are exceptions, but in every other era of automation, this is more or less what we get. And I'm not convinced this is that different of a component of automation. Aaron, can I ask, what are we working toward? We're building this God level of technology that can do almost all of our work, and in 20 years, we're still going to be working. So why are we going to do that?
46:59Well, so it's funny. So, I mean, you should have invited maybe Sam Altman up here because he's welcome to come. His answer will, what's that? He's welcome. Okay, good. So I think his answer would just be different than mine. I think he would say we get closer to a higher level of species where we're not having to analyze the market trends. Like the computers are just doing all of that. and he is much more futuristic on this dimension. I'm not sort of sure I understand why we wouldn't just sort of ultimately consume all of the work the AI is doing as people and then just still want to do more than what the AI did.
47:43But it's very possible that there's some crazy step function change that I'm not imagining that Ilya saw, the other Ilya, and is like, you know, at that moment, then everything really kind of changes completely. But, you know, this was five or 10 years ago. I mean, people like Vinod, I think Sam to some extent, you know, had a view that maybe we end up having UBI in the future because AI is doing a lot of these tasks and then we will just sort of share the benefit of that productivity back to society and humanity. And I don't even necessarily know if that would be a bad outcome. I just don't necessarily think that's the one that will happen.
48:20I think people will just find a way to have other people that they want to work with to go and produce things and to innovate and find the next kind of set of problems we want to solve. Cool. All right, we have one here. My name is Adam Manzoni. I'm the CTO of Funwall.com. You guys know each other. We do. Great user of the platform. Yeah, good to see you in New York. Yeah, we've definitely at Funwall.com, we've seen the value of doing things like programmatically extracting information from things like bank statements. And obviously you think a lot about content. And it sounds like you're thinking a lot about, you know, GPT-5.
49:02And even if you watch the GPT-4-0 demo, you saw like basically computers now have eyes, essentially, that we don't have to change. like in the past Vision models had to have training done to do what we saw in that demo. When you think GPT-5 and all the content that Box stores and has, I mean, one thing I'm really excited about is video, but are there other use cases that you see unlocked on the content layer with these newer, higher-performance models? Yeah, so I think, again, if you go back to the earlier framework of, let's just say, cost, quality, performance, and context window. And GPT-5 for me is just kind of a shorthand for way better AI.
49:54So maybe it needs to be GPT-6 for the thing I'm talking about. But when you have those factors all improve, so AI is 10 times cheaper, 10 times faster, 10 times larger context window, 10 times better intelligence, the thing that we think about, you know, given the business that we're in is what do people do with, with their content today? And, and what if you had effectively AI agents do many of those things, you know, on our behalf and, and thus I can, again, throw compute at the problem as opposed to people at the problem. So that, that is, you know, some of the most straightforward things, like just, I want to review every contract in my business and understand like all of the risk in my business against all the contracts I have or every contract that is up for renewal.
50:43In your business, that's a version of just things like, okay, every single loan that is coming in, I wanna review everything about it and be able to quickly have some assessment of that information to make a better decision. Right now, we're limited by just all the things, all the aforementioned things, which is like, how much data can I put in the window? How kind of intelligent is the model itself? What is the cost for doing that? And if those go away, then we can basically deploy AI agents to do a lot of the kind of, you know, frankly, very manual, not very strategic, not very differentiating work that either we all spend our time on or our colleagues spend time on and at a scale that was just never possible before.
51:25You know, I can deploy a thousand legal review agents at a problem instead of the one person in the legal team that can spend time on this. that's just a totally different way to solve business problems inside of an organization. So you kind of just put that across everything. And this is also why I'm just extremely optimistic is, as we heard earlier, if you're in SF or you're in New York, let's say, the access you have to the best law firm or the best marketing agency, this is a fantastic level of access and networking that we have. but whether it's a startup somewhere else in the world or they maybe didn't get as much funding or they're not in this sort of flow of what's going on, AI as an example, this is three to five years out for this idea but if AI can basically do the things that are usually those first steps to just getting started with a business that I didn't have access to before because maybe I'm like a three-person startup in some, name your country, now I can actually have an outbound sales team.
52:30Now I can actually scale my engineering more effectively. I think this is a massive boon for any small business, any startup, any team that wants to experiment. And I don't think it's going to take from jobs because those startups previously, they actually were not hiring those people. They were just sort of stuck in whatever they were currently doing at a certain scale that they were at. So I think this is going to be an incredible asset for anybody getting started or scaling up. All right. I definitely want to give people more time to hang out and mingle and we'll still we'll have more pizza and beer in the kitchen in a moment.
53:04I also want to say that 15 years ago, I started coming to tech meetups in New York City. I was early in my career and we got a chance to hear from some of the luminaries, people who were really pushing the cutting edge forward in the technology world. And we saw people from those tech meetups end up advancing to places within the big tech companies and in media. and one of them became a really noxious internet troll. But most of them ended up being great and productive members of our society. And I think that there is a real value in bringing people together. It's so cool to see so many people here, many subscribers of big technology and some people that we're meeting for the first time.
53:40And this is gonna be a tradition that we're gonna start here in New York and around the world and hopefully we'll come to San Francisco soon. So thank you all for coming out. Woo, let's hear it for you.
53:54And that being said, I think it's just been such a great privilege, Aaron, to be able to speak with you. I feel like every time we schedule the podcast, some crazy stuff happens in AI. Now, maybe that's just because things are happening every week, but it feels like we always end up with the peak. We have been well-timed in these, but I also, I really, I mean, this is, we started boxing in 2005, and I have never seen anything like this. the amount of just sleepless nights on, you just have to catch up to like three different company keynotes in one day. It's just like, it's insane. The amount of innovation that's happening.
54:29I think, you know, 95 % of it is good. 5 % of it is like very stressful. And like, oh my God, like you never feel like you're moving fast enough and you're not catching up to the right thing. But most of it is just like, wow, what a lucky time to be witnessing all of the technology change. Absolutely. And thank you for helping us unpack it to understand it. So thank you, Aaron. Thanks for having me. Appreciate it.
54:54Cool. Thanks, everybody, for listening. And we'll see you next time on Big Technology Podcast. Thanks, Aaron. Yeah, thank you. What the hell is going on right now? And why is it happening like this? At Wired, we're obsessed with getting to the bottom of those questions on a daily basis. And maybe you are too. I'm Katie Drummond, the Global Editorial Director of Wired. And I'm hosting our new podcast series, The Big Interview. Each week, I'll sit down with some of the most interesting, provocative, and influential people who are shaping our right now. Big Interview conversations are fun. I want a shark that...
55:30That eats the internet. That turns it all off. Unfiltered and unafraid. So in a lot of ways, I try to be an antidote to the unimaginable faucet of reactionary content that you see online, to the best of my ability. Every week, we're going to offer you the ultimate luxury of our times. meaning and context true or false you Brian Johnson the man sitting across from me one day at some point as of yet undefined in the future you will die false tell me more listen to the big interview right now in the same place you find Wired's Uncanny Valley podcast subscribe or follow wherever you get your podcasts
From the publisher
Aaron Levie is the CEO of Box. Levie joins Big Technology Podcast to discuss the implications of AI getting cheaper and faster after OpenAI cut GPT-4o's prices by half and made it twice as fast. We also cover AI's impact of AI on jobs, the evolving AI safety debate, and how companies like Box are harnessing these powerful technologies. It was our first public event and such a blast to meet so many of you! Hit play for a thought-provoking exploration of the AI cutting edge, and what comes next.
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