Meta's New AI Model, AppleGPT's Potential, Is ChatGPT Getting Dumber — With Aaron Levie

25:18And so what is that, you know, architecture? How should it plug into into search? Those kind of things, I think, I think make sense that they're taking their time to get right. One of the things that was really interesting in the story is they talked about how Apple views this. It's getting into this because it doesn't want to miss a potential platform change. and I think that just kind of says so much about where this is heading. I still don't think it's necessarily, like we have to see this happen, right? Like there are still open questions again, like we talked about in the beginning about like whether people want to use like the chat function to begin with.

25:50I mean, I think they'll probably gravitate towards that, but it's just kind of interesting to see how seriously Apple views this. And my hunch is what's going on in Cupertino right now is they're looking around, they got some Vision Pros lying on their desks and they're like, God damn, we just spent five or seven years trying to build for the next computing platform, which was the metaverse or whatever AR VR. And this chat thing just surprised us like a lot of people, right? Maybe meta the same way. And now they're saying, all right, let's, let's get the, you know, get it going on that front. Yeah.

26:24Yeah. I think, um, uh, I, I, uh, I, you know, the good news about Apple is, is, uh, again, they, they, while they take the time to get things right. When they then decide to turn on the engine, they see it through and then we'll commit to it. And I'm pretty confident that they've got the right kind of prowess to do that if they decide they want to. But they haven't really been able to like show that they can really excel in a paradigm shift of technology. I mean, I'll just take the voice computing thing, which is most recent, right? Like, so you had Amazon and Google. They did. I mean, obviously it didn't turn out to be, you know, what a lot of people thought it was going to be, but Amazon and Google took the lead.

27:04They come out with the HomePod and flops. So I don't know if they're going to really be able to navigate this change. It seems like they're very good, and maybe this is an archaic argument, but it seems they're very good at refining the iPhone and less about thinking about brand new areas of computing. Although the Vision Pro was impressive. I still don't feel like they can do it. Well, I would say this is, I mean, this sort of benefits from a lot of the iPhones. This actually dramatically benefits from a lot of the iPhones kind of innate characteristics. So I wouldn't actually think about it as sort of like out of left field or orthogonal to the iPhone.

27:46I think it builds on, you know, like, again, literally, if you just, I mean, voice might even be the right way to interact with this for a lot of for a lot of the use cases, you know, versus like an actual chat interface. And so if you think about just what they are built for, we, again, are all, you know, you know, a second away from being able to talk to our phones and ask our phone a question and get an answer back. And so and, you know, at this point, you know, building an actual large language model is extremely well understood. One of the interesting lines that I didn't actually fully realize or understand like a year ago, but now I like I'm a lot more steeped in from one of my kind of top AI friends is there's no secrets in AI.

28:32And and it's because everything is such a research forward, research first kind of approach in this space. And so if you have a space that really there's no secrets and actually right now the skill set that seems to be required is capital. And then, you know, 10, 20, 50 AI engineers, but not a thousand. That's something that I think Apple is extremely capable of putting together and then designing an incredible user interface around. So I'm going to take the other side on this one. And I would be I'd be bullish on on their approach, whatever they decided to do here. We're going to go to one of my favorite segments when we have you on the show, which is read Aaron's tweets or read his threads, because this is applicable.

29:11So this is talking about moats. You say, I've probably spent more time debating where AI moats will be created than perhaps any other tech trend to date. There's something quite fascinating about AI where the deeper you are in the stack, like training models, you have risk that there's a technical breakthrough and that leapfrogs your approach. and the higher up in the stack, like thin wrappers, you're at the mercy of platforms not competing with you. There will be trillions in value generated, but it's too early to tell where. It's very interesting. I mean, we have all, I mean, you basically have all these big tech companies and smaller startups are all, like what we've talked about up until this point.

29:47We talked about Facebook model, we talked about Apple model, we talked about Google's. The question is like really like where the points of differentiation are. So you've had these debates about where the modes are, who's going to really come up on top and profit from it, what are you leaning towards now? I mean, I know we don't know yet, but. Well, so I'll give you the two obvious ones. And I hate to bring them up because they're so obvious. So let's, and so the first one's really quick. So, you know, at the NVIDIA level, an obvious winner because at the end of the day, you know, all this stuff has to run on, you know, at least today GPUs.

30:24And there's a very small number of relevant players there. So you kind of know the infrastructure winners just automatically. On the opposite end, you have the existing software that already has data. It already has users. It already has workflows. And AI becomes this kind of booster into the software that makes that software more useful, more valuable, more intelligent, more functional. And, you know, for all intents and purposes, I mean, I'm sort of, you know, talking my book. But it's going to be, you know, SaaS products probably in the enterprise space. It's going to be the Service Nows of the world, the Atlassians of the world.

31:02You know, we believe Box and other players because we can plug in AI into our software and just make that a much better experience, be able to solve problems for customers that they couldn't solve before. So those ones are pretty obvious. The reason for the thread was on one hand, and this is actually, I mean, again, like it's funny how fast these things age. Like this was before Llama 2 that I wrote that. You know, imagine you're going down a particular architecture paradigm and you're an AI model trainer. So you're at that level of the stack. Llama 2 comes out. The entire world all of a sudden puts all of their energy, maybe temporary, but they put all their energy on Llama 2.

31:44And they're like, oh, this is now, you know, God's gift to open source, you know, AI models. very quickly, if you were training an inferior, you know, open source, you know, LLM, you're art, you might already be on the wrong architecture. And now you have to kind of move over to LAMA as the, as the new, you know, sort of as the new, you know, model that, that you, that we're all going to kind of be building on. And so the speed at which that happens in this industry, I've never seen before in terms of, of how quickly everybody will kind of just shift their, their, their lens on the, underlying technology.

32:18Conversely, all the way up the stack, I think we've already seen examples where a company that maybe was building a very lightweight interface on top of the LLM of like, okay, we're going to give you an interface to produce text for some use case. We're already seeing that unfortunately the actual underlying product providers are kind of incorporating many of those features into their products, whether that's ChatGPT or Bing or whatnot. And so then it's very, very hard again to survive as kind of one of these kind of quote unquote thin wrappers. So I think that was sort of the reason I wrote that out is like, you know, it seems pretty tough to be an AI model trainer unless you are meta, Google, Microsoft, OpenAI, Anthropic.

32:59And you definitely don't want to be one of these just pure thin interfaces on top. You've got to really establish a high degree of value and probably workflow and kind of data moat associated with your product. Yeah. And that thin wrapper thing, I mean, it's really unbelievably competitive. and the thin wrappers. I mean, when OpenAI came out with ChatGPT plugins, it was just obvious that like, you're going to have companies that will build and maybe they'll have some proprietary like relationships that they'll be able to build into the AI. Like if you're like, for instance, I don't even know, like a kayak potentially, like you have the relationship with the airlines so you could have this one bot.

33:35But even that just like, seems like it might be subsumed by AI. But the things that really seem like in jeopardy are like the character AIs where like you can, you know, chat with George Washington or whoever, whatever historical figure, like you can really just go into chat GPT and say, you're George Washington. And next thing you know, you might have something on par or like you're a Jasper, which allows people to like write better with AI. And you could just go to chat GPT and say, help my writing. Yeah. So, so, you know, I might separate those, those two slightly. I think, um, uh, I think on the character, well, actually, actually I'd say both of them have a path to differentiation.

34:12Um, uh, but, but I'm not close enough to either, uh, to, to understand where that value would be. But, but, you know, in the character AI thing, you know, I think there's probably to, to the user of character AI, there's probably something psychologically different about going to chat GPT and saying, pretend that you're this person and then interacting versus, you know, sort of the, the modality of character AI is sort of you're, you're jumping right into that. And there's a little bit of a community or network effect around those characters, but I'm not, I'm not close enough. So, so I'm kind of freelancing on that answer, but I, and on the Jasper thing that, that was sort of one of the, one of the, you know, obviously Kate, you know, current case studies of, of, you know, all like they had this kind of, you know, lock on a market, let's say a year ago, which is, I want to write an SEO blog post, you know, on some topic and, and they built like the best workflow interface for doing that.

35:01OpenAI comes out or ChatGPG comes out and it's basically completely free. You just don't have the workflow on top of it. And so how do you kind of provide enough differentiation? And so I think there's going to be some really interesting kind of competitive game theory lessons that these companies will have to figure out as we witness the dynamics in this market. Aaron Levy's here with us. He's the CEO of Box. We're talking AI. We've touched on Apple and Meta to begin with. on the other side of this break, Microsoft, Google, and then plenty more. We really have a lot to talk about. Let's talk about jobs on the other side of this break.

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37:17And we're back here on Big Technology Podcast with Aaron Levy. He's the CEO of Box. Let's do some more news, shall we? So Microsoft this week, shares rose 5.8 % on Tuesday, just in a day. This is from CNBC. After the company announced new artificial intelligence subscription services for Microsoft 365, the company will charge users an additional$30 per month to use generative AI tools in Teams, Excel, Word, and other places in their product suite. I don't know. First of all, I think there's levels of insanity all the way down on this one. The stock market sending Microsoft stock 5.8 % to the north.

37:56I mean, it just seems like you say AI, and I'm actually kind of curious how you feel about this as a public company CEO. You got to be careful, right? You got to keep the market's expectations. Like, you know, now Satya Nadella is like, dang it, like we did this one product now at my company. Now I have to justify 6 % more in expectations. So that to me is bananas. And then the other side of it is$30 a month to do this. I mean, you can go over to the Google office suite and I'm part of a test group now where in labs, you can just hit like, write it for me. And Google docs and Gmail will give them a prompt and there there's your email or there's your document.

38:33So it's like, wait a second. That's a lot of money for something. Again, going back to our themes, you know, no moat. What do you think? Well, so I would separate the pieces out. So on the market's reaction, I'm, you know, if everything goes as according to plan for Microsoft, actually, the market's reaction weirdly makes, I mean, it just mathematically makes sense. So let's say they added, I'm going to just make this up, you know, they added$100 billion of market cap or something, whatever the number is, you know, from that or$150 billion in market cap. Yeah, no big deal. But if you just kind of multiply sort of the expected revenue generation from that uplift in price on their Microsoft 365 subscriptions, you kind of have a certain expected profit margin.

39:21You multiply that profit margin by what they're trading at in a P-E ratio. And like weirdly, it actually probably just like checks out that like that is sort of what they actually did add in terms of market cap. If they can actually generate the revenue and profit that's tied to this increase. So that aside for a second, in terms of, you know, will the market bear that price? That is like, you know, we'll find out pretty quickly in the next, you know, six or 12 months as customers start to go through these upgrade cycles. I think it's a pretty steep price. But also Microsoft is, you know, a company that has a very strong command on the customer base.

40:00And we'll see, you know, sort of what ends up happening. I think right now there is a moment where IT leaders, CIOs have to continue to show that they're solving AI problems. That's sort of why Copilot, I think, has gotten a lot of support from the market. This is why certainly our number one conversation with customers right now is on AI and customers trying to figure out, okay, how can I use AI on my content in a secure and safe and privacy-oriented way? And I think Microsoft will benefit from a similar set of conversations across the productivity stack. But yeah, I mean, they are certainly taking advantage of this moment in the market.

40:37Yeah. And what about the fact that you could go across the street to Google and get those similar services for free? Yeah. You know, in reality, any midsize enterprise or on up will not make the switch on the basis of AI pricing. So if you're a if you're a, you know, a 5000 person company and you're already on exchange and you're already on office and you're already on teams, AI will not be the determining factor as to, you know, kind of switching out your entire infrastructure. So I think Microsoft knows that and they're in a good position to kind of benefit from that effective, you know, kind of stickiness of the platform.

41:11There's another story that I wanted to lead with that kind of I had to push down because we, I mean, every big tech company is making news on this this week. But let's talk about it now, which is the, it seems like people are saying that chat GPT or the GPT models are starting to degrade. I mean, this is from Insider. It's very interesting because there's so many different ways to read this story. I'm very curious what you think. So it says, in recent weeks, users of OpenAI's GPT-4 have been complaining about degraded performance, with some calling the model lazier and dumber compared with its previous reasoning capabilities and other output.

41:46It's so interesting. Like I have this theory that these chatbots have spent, you know, they were spending their time talking to open AI engineers and they were freaking smart and they learn from the people they speak with and then they got released to the general public. And yeah, they're dumber. So that's one possibility. The other side is that maybe they don't seem as amazing to us the more that we chat with them. What do you think? Well, I don't remember where I saw this. I'm pretty sure it was. I'm pretty sure it was from an open AI employee tweet. tweet. But if I'm wrong on that, then obviously don't go off this.

42:20But I'm pretty sure an opening I employee claimed the second explanation, which is, you know, we're now used to this. And so if you kind of imagine, if you imagine like going from no Chachaputee to Chachaputee, like it's like that level of jump. And now and then and so now this is our new baseline and we're kind of watching it over time. And our brains are not having that same instant kind of reaction of like, holy crap, this is the craziest thing of all time. We're now in this sort of expectation mode of like, yeah, you're going to produce text for me with my question. And so I think now we are a lot more, we're a lot more critical of now the information we're getting back because the core novelty is now over.

43:03And now it's actually, we're like fundamentally in the, you know, are you telling me very useful, how reliable information. And so I think our level of criticality has just gone up because there's no kind of novelty factor that's sort of overweighing or outweighing the critical, you know, kind of, you know, maybe component that would, we wouldn't have had nine months ago as the technology was new. So that's, I'm actually more probably in the camp of the latter. Um, but you know, who knows, maybe they did some kind of training update or, or, um, uh, or, or, or model weights update that, that made it dumber.

43:33But, um, but it seems like it might just be our expectation level is now, um, is now off relative to, to, you know, where these models are. Yeah. And there has been like a decline of interest that you see in Google search trends and there's been a decline in use, at least according to the slowing of growth, according to some third-party reports. We have a professor from Wharton coming on on Wednesday, Ethan Mollick, who is going to talk about the impact of this on education. I'm just going to plug that, but it is, there have been some people saying it's just, oh, it's the kids there and they're not using it to cheat anymore.

44:04I'm curious what you think, what you think we should make of these reports and what has your personal use been like? Have you been using these bots less in the past few months? I know I have. Yeah. I mean, so I'm probably, my usage pattern has actually been pretty constant other than the first week where I was just testing everything and I was just typing in the dumbest stuff. I mean, I would say I am a regular user for business, like some category of like business brainstorming. So if it's like 1030 at night, and there's nobody, you know, in the company that I can kind of quickly run an idea off of, or ask a question, I might go to ChatGPT and, you know, brainstorm a product category or a name or, or, you know, help, help me to kind of synthesize some of my thoughts.

44:54And, and so I've been pretty much commonly using that for, for the entire existence of the product. Um, uh, we had a, uh, we had like a goat, we, we had what we thought was a gopher problem, um, in our yard, um, uh, like, uh, like two months ago. And I was, I went down like a gopher rabbit hole. Was it, you thought it was a gopher problem. It wasn't, it was like a hedgehog problem instead. Well, we, we never validated actually what it was, but, but I learned a lot about gopher holes, um, at GBT. So I really hope it was not hallucinating. You went down a gopher hole, rabbit hole. No, I'm sorry.

45:27Exactly. Exactly. So, so I think, I think it, you know, what's interesting is it very clearly is it's, it's different than search. Right. And so, and so like, I don't know that we even, I still, you know, in July of, of 2023, I still don't know that we've got a perfect term for what category these things are in. It's, it really is this new emergent, you know, kind of, you know, technology use case where you have this intelligent thing that you can go and ask questions of and help you kind of work through your own thoughts or help you sort of figure out what things you want to then go and dive into.

46:09I've done a lot of things where I'll go to chat GPT, ask a set of questions, learn about a set of things, and then go to Google to then go dive really deep into the research on whatever that topic is. And so it clearly is not a one-to-one replacement as much as a new compliment to sort of working through information. And I think what we're probably finding is actually like, like, you know, that is not, um, that's not a use case. Maybe you do five times a day in the same way that you're Googling, like, what time is this restaurant open till? Um, uh, and like, and so that, that's just like a different behavior that, that we don't, you know, we don't do these like broad information, you know, sort of understanding type scenarios, you know, five times a day in our personal lives, at least.

46:52There are a lot of companies that I think are trying to pull a fast one on everyone in terms of saying, you know, we're in a moment of, you know, companies trimming down and doing layoffs. And obviously another moment where if you say AI, the market will reward you and they'll reward, they have been rewarding companies for layoffs also. And some companies are trying to like blend the two into one. I mean, you had IBM, for instance, that said like, oh, they're not going to hire anymore because they're going to give these tasks over to AI. And I had people from IBM or who had been in IBM say, lol, like that's not going to happen.

47:25This company is not able to pull that off. And even one of your competitors, Dropbox, right, they had to do a layoff and they said that they're going to pivot to AI. And this is from Drew Houston, the CEO there. our next stage of growth requires different mix of skill sets, particularly in AI and early stage product development. So, okay, it's people building AI, but you know, there's been this meme, chat GPT is going to take your job. It hasn't really yet. Why not? Yeah, I'm, I'm, I'm firmly until proven otherwise by some breakthrough I haven't seen yet. I'm firmly in the camp of this is all net positive to jobs.

48:03I think that, you know, first of all, we are so early on on any form of kind of like multi operation tasks string together with any level of efficacy to be able to replace even even like 10 minutes of what a real person does in their job. So right now, I mean, you really got to think about these things as they can take they can do like one discrete information oriented task basically at a time before they need a human to kind of review what they've done and then move to the next thing. And again, I don't really see an architecture paradigm that would change that anytime soon. And so basically they can do tasks where they take some information, they go and look across other information, they can produce something.

48:55And that, that thing that they produce can go somewhere certainly automatically. Um, but, but usually you very quickly need a human in the loop to, to, to review or kind of help streamline whatever that was. And so that's just not that many jobs are, are relegated to only that kind of thing. Um, uh, even, even in the, uh, even in the scenarios where we've kind of like tried to say, okay, well, the paralegal job, um, you know, could, could be automated or whatnot. It's just, simply not true. There's just too many things that these job functions are pulling together across email and another communication tool and a manager and going into some other system and then reviewing something and then writing something.

49:35And the AI is just not, we're just nowhere close for the AI successfully to wrap all those scenarios together. And I'm just not seeing anything on the horizon that would change that because the cost of an error is so high that no one is willing to put that, you know, the liability on the line that would be required. I mean, look at just even that one example of the lawyer that, you know, used ChatGPT and hallucinated cases. And now they're like, you know, they've been sanctioned or whatever the consequence was. You know, you can't have paralegals running around everywhere being replaced by AI, just producing, you know, all this stuff that doesn't get reviewed.

50:14And then we just put it into the legal system. It just simply won't happen. So, so I'm, I'm not convinced that I've, so, so, so that's the, the, that's why the, the doomer scenario, I just don't buy right now. And now actually you flip it to the optimistic scenario. And I actually think more, more of our economy is constrained by either talent or the cost of, of, of that talent, not by the demand for, for the, for the talent. And so I think in most areas for the, of the economy, if you could make something 20 % cheaper or, or conversely, 20 % faster, you would probably use more of that type of resource or service than, than you would, then, then instead of kind of capturing the savings.

50:56And, and that, that is, you know, there's, there's sort of, you know, two, you know, there's, there's sort of two complementary

51:05economic rules or fallacies or principles that are at play. You've probably written or talked about both. You have this idea of lump of labor fallacy and you have this Jeevan's paradox fallacy, which is both of them basically amount to, we tend to think that if we made something more efficient, jobs will go away. And actually by making that thing more efficient, demand rises because we've been able to actually get more output out of that thing. And actually the cost of that thing was the main reason why we didn't use it previously. So, you know, even in the example of like an engineer, let's say, if I could ship 20 or 30 % more code, then we're not going to have 20 or 30 % fewer engineers.

51:48We'd probably hire more engineers because our main rate limiter right now is how fast we can ship software to generate more revenue to hire more engineers. Right. So like this is where we got it all backwards, which is most areas of our businesses, we would actually accelerate growth. We'd accelerate our business if we could make the underlying thing that we do faster and more efficient. And so I'm much more in the optimistic camp. I basically think the pessimistic scenario I'm not seeing and I just I don't think it's going to happen within this architecture paradigm 20 years from now. Sure, let's do a podcast and find out where we ended up.

52:24But right now, this is not something that I feel we have to worry about. Okay. I'm just going to ask you two questions to wrap up. These are two I definitely wanted to get to, so I appreciate you taking them. The first is about where Amazon sits in all this. They've been very quiet on the consumer front. They're actually building GPOs, which no one talks about. And their sort of position is that they want to make this something where they can work with their customers to help them build those custom off-the-shelf models. So I'm curious. I'm just going to ask you both of them, and then we can get to them.

52:54I'm curious what you think there. Second thing is you had this thread that said, if Sarah Silverman ends AI, that's going to be absolutely wild. She's sued for the use of her content. And I'm working with an editor and he said that I should ask you, aren't AI companies just vampires sucking value out of human creativity, putting people out of work and returning very little in terms of value to humanity? Why should we tolerate that and who will benefit from it? So it goes back to, I think, like the intellectual property thing. Okay, that was a lot. and feel free to answer as succinctly as you can.

53:26I want to make sure to be respectful of your time. Sure. Well, on the Amazon piece, I would say I probably can't fully describe where they want their role to be. I can describe where the role is now, which is they're clearly going to be one of the at-scale, hyperscaler infrastructure providers hosting models from a variety of vendors and providers. And I think that's a great spot to be in. They can host Llama, they can host Anthropic, They can help customers fine-tune Llama. You know, there will certainly be, you know, a thousand times more AI models that could run on Amazon that are open source than sort of ones that are commercial and proprietary to one particular vendor.

54:09And so their strategy is just to say, hey, you know, bring on all of the AI innovation in the world. Let a thousand flowers bloom. We'll host all of that. And that's, I think, a very practical, you know, strategic move. you know, do they go deeper in their own AI models? Do they help customers train models with additional services? You know, probably. Um, but, uh, but, but I, I don't know beyond kind of them as an infrastructure player where they want to sit, but even in that, even in that position, I think they're really good at that. Um, and, um, and I think they'll, they'll, they'll continue to kind of profit and, and build a very large business on just that role, uh, alone.

54:43Great point. Sarah Silverman. Sarah Silverman. Well, so, uh, so I guess a couple of things. One, you know, most people that are at least on the commercial AI side, I think are at least contemplating or trying to figure out a way for the Sarah Silvermans of the world or the content producers to get paid something. At least that's the high level commentary. I don't quite know technically how anybody accomplishes that. But I think that, you know, I don't think the intent is to be a vampire, uh, as it were now, now, um, uh, I think, I think the other, the other thing is, um, is, you know, it's, it's unfortunately just a repeat of the prior conversation.

55:23I just don't think they take the job of the screenwriter, uh, or the joke writer or the animator. Um, I think they act as a way to, you know, boost or amplify or accelerate the work that, that one of those individuals are doing. You know, I was chatting with someone in the AI space doing kind of video AI. And, you know, you're a director or a cinematographer, and you have to go kind of figure out what's the shot you want to go do for some film. And, you know, you spend X amount of time thinking that through and, you know, maybe, you know, storyboarding it. This, you know, the capability you now have is you could look at the simulations of that and you can look at a thousand of them or a hundred of them and then pick out exactly the kind of shot you want to go with and now your creativity combined with the automation benefit of of seeing lots of options and testing different scenarios we will actually just get better content from that amazing cinematographer or director not because of ai because ai made them more efficient in in something that would have taken months or quarters or whatever of time now is sort of shrunk into a much shorter period.

56:35So I think we sort of imagine AI is going to go replace those types of jobs, and it won't. It will be another tool in their arsenal just as every new technology kind of breakthrough has become another tool for the Jon Favreau's and the Steven Spielberg's. This just becomes yet another way for them to continue to make better art and better content than they did previously. So I'm pretty optimistic on all of the kind of creative flourishing that we're going to go see from all of this. Not to mention just, I think we'll see this as a multiplier of content. And anybody who argues that it wouldn't be good to just have more people be able to produce good content, then you're kind of just gatekeeping.

57:18And you're sort of just trying to constrain who should be able to make a film, who should be able to be creative, which obviously doesn't, I don't think is practical or reasonable. Aaron, every time we talk, it seems there's just this avalanche of interesting AI news. So let's keep it up. I mean, great speaking with you as always. Thank you for being here. Good to see you. Appreciate it. Awesome. Thank you, Aaron. Thanks everybody for listening. We'll be back on Wednesday with my conversation with Ethan Malik, professor at Wharton, Wharton College and University of Pennsylvania. Thanks again for listening and we'll see you next time on Big Technology Podcast.

57:55you