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Big Technology Podcast
The Fable Ban's Unintended Consequences + AI's New Economics — With Aaron Levie
00:31:24
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- [00:00]
A
No one goes to Hank's for his spreadsheets. They go for a darn good pizza. Lately, though, the shop's been quiet, so Hank decides to bring back the $1 slice. He asks Copilot in Microsoft Excel to look at his sales and costs and help him see if he can afford it. Copilot shows Hank where the money's going and which little extras make the dollar slice work. Now Hanks has a line out the door. Hank makes the pizza. Copilot handles the spreadsheets. Learn more@m365copilot.com work and Aaron Levy is the CEO of Box. He's one of the most insightful and fun voices on the future of this technology. He actually was the fourth guest ever on big technology podcast and the guest the only other time we did a live podcast together. So with that, I am thrilled to welcome Aaron Levy. Please join me in welcoming him. Aaron, great to see you.
- [00:55]
B
Good to see you. Thank you.
- [00:56]
A
Let's dive right into it. I'm going to start again. Since you're not at one of the labs. Let's start to talk about the biggest, most controversial moment now, which is the anthropic fable situation. Let me put to you what I call the Jassy mystery. So what we know about.
- [01:13]
B
I'm sure he likes that name.
- [01:15]
A
Well, he didn't show up, so we can talk about it. Okay. Okay. So what we know about the fable ban or the export controls on Anthropic is that Amazon found a vulnerability in the software and Andy Jassy maybe made a call to Dario, definitely made a call to the White House. And then very soon afterwards, there were export controls that were put on Anthropic's frontier model.
- [01:39]
B
Those two fact patterns are probably not ideal.
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A
So the mystery is why did he do that?
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B
Yeah.
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A
And here's one hypothesis. This is from Chamath. He said, Google, Amazon, Microsoft, Meta now have a serious non zero opportunity to to tank the frontier labs. Go to the government, kneecap the lab's motion of putting the latest models out into the wild, become the trusted gatekeeper between labs and the public by having the labs go through their clouds.
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B
Okay.
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A
Plausible,
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B
I would say. I mean, anything's plausible. I prefer Occam's razor on this one, which is ever since Mythos. You know, Mythos very clearly was this event that basically said, you know, AI is obviously getting super powerful. It has all these risks associated with it. We are going to give it to some, you know, small trusted partner network. They're going to go evaluate their own tools, they're going to evaluate these capabilities. There's been a lot of sort of. It's a very kind of dramatic, you know, kind of rollout of a technology. And I think what that has done is it's created this flywheel where it almost incentivizes even more drama and more research and more depth in, you know, security being the primary space in a way that we could have already been doing since GPT4 if we wanted. You can go and deploy these things to go find lots of vulnerabilities. You can use them offensively or defensively. But Mythos kind of created that extra air of seriousness and uncertainty around it for good reason, because it's an incredibly powerful model. So I go with Occam's razor, which is. Amazon obviously has security research teams. Any company at that scale, we try and test and push the limits of models and our particular domain of use cases. Clearly, at Amazon scale, you have a very large security team. They're trying to jailbreak models all the time. And so almost by definition, there's already a public private partnership on all forms of jailbreaking models, trying to push them to the limits. As a part of that, and especially with the surrounding atmosphere of Mythos, I think it would be very natural for Andy to either share that research or his team to share that research, and that escalates, and then that sort of creates its own flywheel. But the idea that there's some kind of board, boardroom level sort of strategy meeting that says we now need to kind of like co opt the technology, become the only interface of the government, this kind of puts us in the pole position. I think it's less likely that and more likely this is a situation where the Mythos momentum continued. Fable obviously had ways of getting back to the Mythos level capability, and researchers sort of shared that information. And I think there's like a basically, you know, very limited small percentage chance that then Andy and team knew that, like, the very next event would be they'd stop the model. And that's not even good for Amazon. Like, strategically, like, Amazon makes plenty of money the more that Fable gets used in the world. So I don't think you would. I don't think you would do some kind of like, you know, kind of maneuvering to create this. So I kind of just go with this is it's a very chaotic kind of environment right now. The government has, you know, only a few tools at their disposal at any given time to deploy against these things. Those are going to be kind of blunt instruments. And this stuff is coming together very quickly because of, in some cases, the lack of technical capability of the government compared to how powerful these models are. It's like you don't. You know, when you see something that seems very scary, like, oh, my gosh, the thing could be we can jailbreak the model and get back to Mythos level capability, and Mythos was the thing we're supposed to be scared about, then, you know, just like, stop it like that. I think that's like a very natural reaction based on the atmosphere that we've created in AI recently. So I just go with that as the answer.
- [05:16]
A
I mean, I like that you say the atmosphere that we've created in AI lately.
- [05:19]
B
Everybody but me.
- [05:20]
A
Yeah, well, I mean, it's funny. I didn't create it. The company on the receiving end of this, though, is anthropic. And, you know, you talked about these mythical capabilities. They called the model Mythos. They put in the documentation that, like, it broke out of its containment and wrote the engineer while he was having a sandwich in the park. Is it that surprising that this is one of the downstream impacts? Yeah.
- [05:39]
B
But if you put that in your announcement blog post, you know, people might be able to kind of extrapolate and get pretty, pretty, pretty scared of things. I think it's interesting. So, you know, on the anthropic front, first of all, I have a huge amount of respect for the entire kind of stack of researchers and policy folks across AI. I happen to have disagreements with some of the categories, but I think there's a deep. Let's say if you imagined a continuum of the most, like, if you kind of had the most. I mean, this only in, like, a polite way, it will sound impolite, but, I mean, if you're the most doomer on one end of the spectrum and the most accelerationist on the other end of the spectrum, here's kind of the views. The most doomer possible was afraid of GPT3, and GPT3 was going to sort of accelerate and kind of achieve some kind of unstoppable continual improvement. And the acceleration says we need Fable 20 as soon as possible. So that's. That's sort of the continuum. I'm probably, like, maybe two thirds up to the accelerationist kind of side of things. But if you were on the doomer, and I'm trying to say the polite version of doomer, like, you're deep in AI safety, you're very scared of the technology, you think there's as much likelihood of bad things happening. As good things happening, we have to win the race and control and kind of stamp down on the technology. We don't want this to be this sort of thing that runs in the wild. If you're in that end of the continuum, the thing that happened this weekend is actually the best case scenario for you. So you actually want there to be these sort of valves and buttons in the government that just is like, we're just gonna stop it.
- [07:19]
A
I mean, that's Dario's position. Do you think he's happy with what's going on?
- [07:22]
B
I'm not gonna. I won't try and guess any of that, but I will just say, if you had to establish a. If you had to establish a regulatory regiment. That said, we are going to review models, we're going to push the limits of models, and we're going to have the ability to either roll back access to models or prevent their release in the first place. We want that to be a regulatory approach. You would need an event like Fable to effectively create the precedent for that environment. You're not going to wait for Congress to vote on this being the new kind of process. You would kind of need something that shocks the system into that kind of regulatory framework. So all I'm saying is that if you were on this end of the continuum, this is actually an outcome that is sort of almost desirable. Now, maybe you would wish that there would be more technical evaluation, more back and forth. Maybe you wish the policy people were different on the other end. Who knows? But the idea that we now have established that the government can press a button and prevent the rollout of AI is actually probably a positive update for an entire cohort of people. Now, unfortunately, I don't know that any of your guests represent that cohort, but I think you could easily get some people that would be like, this is the greatest thing that's ever happened in AI safety. Because now we actually have. We've created the case law essentially for this. We know the tool exists, and then the next messy process is, when should we use the tool again? What should the real kind of ongoing process look like? But I think that probably. I wish this wasn't the case, but I think practically in the next three to five years, we probably have to end up in an environment where models do get evaluated by the government. There is a sort of collaborative approach between the government and research and the labs. The government has to kind of green light the release of the model. I think it's probably become either too scary of a technology or too economically powerful of A technology for governments to not want to be in that position. I think that has massive implications when you kind of unpack it. Like just totally massive implications. One being other countries now have far more incentive to stand up their own sovereign AI initiatives. So it's actually like maybe net negative for the US economic position in AI that this is the outcome. I think somebody could take the other side and say, no, we'll always have the most powerful models. And so this puts us in the best position because now we can do like horse trading with other countries of do you want access to our stuff? So I think it's. I like the fact that this is a super interesting debate and I have like a huge appreciation for every part of the continuum because I think it's so intellectually interesting. I still land on the hey, we probably want to treat this technology more as a substrate technology and then regulate the applied use cases. So we should regulate if you use AI to break into something, we should regulate if you use AI to do bio kind of research that leads to dangerous things. We shouldn't regulate the model itself. But I totally understand the other views that are on the other end of this. And I think it's kind of very natural with this important of a technology that it has to be somewhat of a democratic process of how we decide to regulate it.
- [10:27]
A
Yeah, you remember there were all those petitions, six month pause and everyone kind of laughed at them. Yes, this is effectively the best way to do that type of pause.
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B
Yeah, I mean this is if you were in the pause AI movement. This is again like this is a great outcome. We now have proven how we can pause AI. Now it's an interesting kind of like mechanic that they chose. It's sort of this export control thing. But effectively, if you have an export control where non US nationals can't use the technology effectively, that's pause AI. Because your end API users of these models almost have no way to fully ensure at all times that their end users don't sort of fall into some kind of criteria that's off limits.
- [11:07]
A
And there's already companies that are pulling back. JP Morgan for instance, has told its Hong Kong users no more clot.
- [11:13]
B
Right. Okay, so now if you really war game this out, like two to three, four more years out. This is kind of interesting. So we have this sovereign cloud kind of comparison. But cloud, for better or worse, basically became a commodity. Whether you're running in a cloud in there's lots of performance implications. Some are faster, some are cheaper, but largely you can get a web server Built out wherever you are in the world. You can get storage built out wherever you are in the world. We can build sovereign clouds. Sovereign AI is a different kind of, has some intricacies that are different. Right. Intelligence just is not commoditized yet. We don't have everything having the same model capability. So there's lots of really interesting implications, which is, well, what if one country has access to frontier intelligence before the other country? What does that mean geopolitically? What does that mean economically? Obviously now, if you're another country, you have so much commercial incentive and to make sure that you can build out labs and have access to frontier intelligence as a kind of hedge against the US So who's a net winner in that? Probably China. And so what's interesting is you end up, I don't know if probably most people saw the doorkesh Jensen interview. And you can actually, it's a Rorschach test. You can watch that through two totally different lenses. You can have one lens, which is like, Dorcas is totally right. We have this huge lead. This stuff is so dangerous. But if we control it, then we're going to control everything. The other lens, which is probably more of the Jensen angle, is like, actually these other countries have a lot of incentive to also get this right. And so even if it's like a $500 billion problem for them, they just might deploy that much capital on this problem and they will eventually get it right. And so at the outcome, actually, we haven't gotten any gains as better intelligence from the rest of the world. But what we have lost is our economic superiority in this technology category, because what we've caused is a catalyst for all the other countries to have to build out their own stack. And if they build out their own stack, it's probably going to be chips from China, models from China, et cetera, which I don't have any reason to be against other than just I want America to win the economic angles on this. And so this is sort of this debate that happens on where should you apply export controls and what are the implications of that downstream. And even this week, post fable, we see that you have models that are certainly not Fable performance level, but opus 4.7, 4.8 level, which is a big update for a lot of people on what is now possible with open weight models that we just didn't have visibility into before.
- [13:49]
A
Yeah, I think you shared recently that the open weight model or open source models, the capabilities are not that far away from the frontier. And in fact, as these Models get smarter, they're almost going to saturate with intelligence where there's not going to be such a big difference between say the smartest open source model and the frontier, don't you think? And then so won't this push people to open source?
- [14:10]
B
Well, so the big ongoing conversation, and I think you have some guests that can really represent what they're seeing on the front lines is do you have a sort of fast takeoff scenario of model capability and progress and with some kind of continual learning, kind of self improvement dynamic. And then it stands to reason that the company with the most compute or the country with the most compute and you get the fast takeoff, you get sort of a virtuous flywheel that maybe has some compounding benefits to it that are just unreachable by anybody else. That's a scenario. Another scenario is that that's another just incremental capability. Everybody kind of catches up to it and you always have this sort of two loops going at all times with the closed providers and the open providers and they're kind of always within three to six months of each other. The world is so different from a market structure standpoint. Whether we end up in an outcome where we have sort of an exponential progress in the models that kind of continually learn versus the closed source models and it's like a five year gap in progress and that just goes again kind of exponential, totally different market structures. The one where we have this exponential progress is again, it's probably actually net positive for America in that case. In which case the export controls probably worked. It means our top three four labs have this incredible superiority. We control access to this technology. That's actually a good scenario economically speaking. It might not be a total net good scenario for society, but it's good for the U.S. let's just say that's one scenario. A lot of people are betting that that's where we're at with research. The other scenario, and China sits around and they probably bet on this scenario is no, we're going to be able to keep up. We're going to throw more compute at the problem, we're going to get more data, we're going to build our own flywheels and it's always three months out kind of behind. And if it's three months behind and it's an open weights provider that has more of a commoditization kind of business model approach, because they just want to sell more infrastructure or chips, or they just want to reduce our superiority in the space, which is actually strategic for China to do. Everybody wonders why are they doing this open weight stuff? It actually makes total sense. You're just reducing us's dominance in a field and it might be worth a couple hundred billion dollars to do that for something that might be worth $10 trillion. So if that keeps up, because there is real economic advantage to doing so, then you have this new kind of dynamic that plays out, which is maybe the layer of incremental value shift is effectively the applied layer of AI. So if you think about there's the lab layer and then there's the applied layer, the cursors, the cursors, the Harveys, the Sierras, the decagons, the boxes, which
- [16:54]
A
is amazing because everyone said they're thin wrapper on top of large language models, but now maybe that's where the value comes.
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B
Yeah, and it's one of these things which is like we just have to not be binary about it. Like everything I'm saying, I think the frontier models still make way more money in the future than they do today. Because what happens at the routing layer is you still sort of say, hey, I want Fable or GPT5 5 or whatever the next model will be. I want that to be the orchestrator. I need the super intelligence of the orchestration layer and I need superintelligence at the review and sort of like fix and check the work of the other agent. And so you have a barbell, maybe U shaped model where you use frontier intelligence. But then everything in the middle you can just say, nope, I'm going to take that to Nemotron or QEMI 26 or GLM 552 or whatever. And then all of a sudden it's like you have super high cost inference in one part of the workload, super low cost, still pretty good inference in another part of the workload. But who has the incentive to do that? It's the applied layer of AI. Because the business model of the applied layer is obviously our job is to give you the best model for the job, not just the model from just our lab. It's cool because we actually now have a good push pull between Frontier labs and the applied layer. Where you probably wouldn't want it to be that we're all only in the orbit of one or two companies, commercially and economically. You'd want to make sure that there's some good tension there. And so I think that's kind of the direction things are headed. Between the token costs, the open source models becoming so good, and then maybe even some of this regulatory dynamic. I think the Applied layer sort of incrementally gets more of that opportunity, which is obviously great.
- [18:36]
A
So you've talked about open source and you've just mentioned China, but what can you tell us about La Chanton fat?
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B
It's great memes.
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A
So, folks, La Chatton fat is a rumored open source model from Mistral and has been the subject of great fascination from the Internet, wouldn't you say?
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B
There's great comedy.
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A
John Denk, can we show people what we're talking about? Let's roll image A. This is La Chateaune fat, the number
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B
one model from Europe.
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A
Yes.
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B
Or the world.
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A
My French, rudimentary French. It translates to the very fat kitten. Can we roll B. This is a standard day in Paris now, but it does show something that there's so much eagerness for AI that there's now fan art for this potential model from Mistral.
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B
We've reached a really important phase in the cycle. So I do think that it is kind of cool because some of the things that you maybe discounted the importance of all of a sudden just have so much more importance. Like, I'm watching the. I don't know if folks are watching the fireworks. Base 10 space as an example. Like, it's pretty cool that we now have these open weights models that you can effectively post train on your particular domain of task and you can go and eke out another five or 10 points of performance on these types of models. And again, that's only possible because of the Mistrals, because of the Chinese kind of open weights models. And the cost curve has gone down so much that there are actually some situations which is, oh, actually maybe I should train a model just for my use case because it's literally like economically now, it's not even like I want control. It's actually economically advantageous for you to do so.
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A
So is this the answer to the big token maxing hype where everyone's spending all this money on tokens and not really understanding where they're going or whether there's an ROI that.
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B
Yeah, I mean, I think that in practice that phase probably lasted two and a half weeks from the moment that
- [20:41]
A
Meta tokens token, the Met overhyped token.
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B
Max. No, I would never claim that we should go through your various podcast headlines, but the.
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A
We're not going to do that. We got the cat pictures and that's it.
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B
Yeah, no, but like, I mean, if I had to like capture the cycle of like the first token maxing, you know, Meta has a leaderboard, uses the most tokens possible to now, you know, the last weeks of rumors of like, we're shutting down everything, no one can use AI. Yeah, it's about a two month period. So it's, people need to probably always kind of step back and just be like, okay, is what we're doing a pragmatic thing for work or are we just sort of getting kind of hyped up too crazily on something? What's interesting is this phase was so short that I don't ever think it reached outside of the tech industry. We kind of host these CIO dinners in every city that we go to and we had a dinner. Within three days of the token maxing initial spike on Google trends, the word finally emerged and three people had heard about it. And so I feel confident that it died.
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A
Right. They haven't heard about it because their employees are outspending the tokens.
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B
Yeah, fair point, fair point. But hopefully it will have completely died by the time it reaches the rest of the world and then we can just move to more normal environments. But the thing that is true of the phenomenon is that these agents are just using hundreds of times more tokens than they were before. And so when we launched our first kind of AI use case within box, our product, the average number of tokens that was being used on a task was like 5,000, 10,000, 20,000 tokens. Now our latest agents might use a million tokens or 5 million tokens on executing a task. And so that's, in some cases that's 100x increase in number of tokens. And the reason for that is obviously what's happening is right as we solve one use case, when you would think that we can drive down the cost curve of that one use case, all of a sudden a model capability allows us to now add another use case that's much harder. And then our appetite just grows to solve harder and harder and harder problems. And so it's this funny thing because people get confused. They say, I thought AI was supposed to get, was supposed to be getting cheaper. And it's like, yes, you can actually think about it as cheaper. If you looked at the unit of intelligence, the reason it's more expensive is because we're now taking on bigger tasks. And so we're getting confused because we're like, why is this the one tech trend that doesn't have sort of the Moore's law phenomenon? And it's because actually, no, we're outrunning the efficiency improvements in our appetite for what these models can go and do. And so it's Actually, what you need to do is have like a way to normalize the cost of the tokens to the tasks that you can now deploy. And then if you look at that, then that starts to look cheaper on a per task basis. It's just again, our tasks are getting bigger or more accurate or more effective, and that's going to happen for quite some time.
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A
The reason why token maxing took off as a concept is because people saw the exponential revenue, the fact that anthropic and OpenAI were at 0, 20, 23. Now they're going to do $50 billion this year at the very least. And so people are looking for an explanation. And either the answer is this is real or it's somehow inflated. And that's why people go to token maxing. So if I'm hearing you right, what you're saying is all this spend is much more legit than some of the online discussion makes it out to be.
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B
Well, I think if I had to officially provide my own takeaway for my own point, it would be, it would sort of be. There's, there's sort of always this experimentation phase of a new technology, and this happens to be a relatively expensive technology. So thus the experimentation phase is expensive. And then what will happen is enterprises will deploy AI and then they'll sort of peel off. They'll start to see, like, where are the real use cases? Where are the ones that aren't as real? They'll wind down the ones that aren't as real, the ones that are real. They'll then look at it and they'll say, is there a way to do it at a lower cost once we understand it enough? Or do we still need the frontier intelligence for everything we're doing? And that's actually just a pretty normal, I think, process that everybody's going through right now. But I think about it like our engineering team. We are not token maxers in the sense of there's no leaderboard. We're not incentivizing overuse of tokens. We're just saying use it as effectively as possible to get your work done faster. And our growth rate of spend is exponential. And we're totally happy about it. Nobody internally is, other than we gotta shift some things around and make sure we plan for this even more next year. That's obviously a stressful conversation, but we're not stressed about the idea that we're spending on AI. We're quite excited about the productivity gains that we get. And so I think what's happening is every enterprise is having to kind of go through their own journey on that. They're deploying it in some teams and some teams are saying, oh my gosh, this is the greatest thing of all time. And then other teams, you kind of look at what they're doing, you can't see any kind of measurable improvement in the output of that organization. And so then you're like, okay, maybe it's not as effective there. But I would say, I think it's very easy to capture one or two anecdotes and then kind of over extrapolate on the overall themes. I would say the vast majority of the current agentic spend that's happening is sustainable partly because it's actually coming mostly from engineering and engineering related tasks. And this is an audience that is kind of technically capable of determining whether they like the work product that's coming out of the AI. Maybe as it gets to other parts of knowledge work, those people will not be as familiar with how to do the ROI measurement and then it'll get even messier. But so far I think it's actually been largely totally reasonable.
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A
Okay, we have a couple minutes left. Let's do a small lightning round. So my first take here is that Siri is really good. It's going to be really good now.
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B
Yeah.
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A
What do you think?
- [26:36]
B
I agree.
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A
What? Elaborate.
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B
Oh, is it lightning round or is it like you want to hear a five minute answer round?
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A
You give like a 60 second answer.
- [26:46]
B
I mean, what could be easier than pressing a button on your phone and talking to it? And if you, you know, at least based on the announcement they've taken Gemini, which is a very good model and been able to, I don't know if it's fork or distill or something within there is sort of Gemini grade intelligence. So if you get Gemini grade intelligence and voice on your phone, press a button. I think you're just going to use that for a lot of things. I think the exciting thing is imagine that hooked up to various apps on your phone. You're like, hey, order this thing for me or go and add this calendar entry. I think those are very plausible daily use cases that we will have and it's exactly the sweet spot for Apple to own that space.
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A
Yeah. No, I think Apple did it finally. That was good. That was a good time.
- [27:31]
B
Are you going to tell me if my answer's right at the end of each one?
- [27:34]
A
Okay, that's. Yeah, this is okay.
- [27:36]
B
Okay, so we agree one for one.
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A
How about this one? Permanent underclass.
- [27:41]
B
I don't like this one. This one I don't like at all. Not only do I disagree with it, but I think it's just like a bad meme to have in the atmosphere. I think it's like not good for college students coming into the, into the workforce of having so much stress about what company to join and what's gonna kind of play out. I do think companies actually do the job market a disservice though, by not being as clear on their own philosophies on this, which some of it is reasonable because it's like, oh, man, we're just like, we're getting thrown through a loop. There's so much innovation. But I do think that companies need to be somewhat clear on, hey, here's how we want to use AI. We want to use AI to accelerate our work or accelerate our technical innovation or accelerate our ability. Hit customers versus no, we're actually. Our metric is as few employees as possible. With AI, you kind of do want to be able to have some stance and I think companies have been very confused and that lets this meme somewhat persist for the Internet.
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A
Okay, I won't rate that one. Thank you. All right, last one. Is the SpaceX performance good or bad news for OpenAI and Anthropic?
- [28:54]
B
Oh, well, it's obviously good news.
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A
You don't think Elon took some of their money because he pitched the market on an AI company and that's where the money got funneled into?
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B
I'm not sure I've seen a limit of appetite. I mean, there's a literal limit of money in the world, but I don't know that that is zero sum at this stage. So I think people are pretty clear that if the revenue of this entire category of the frontier models and the infrastructure stack is measured in the trillions, then you can have 20 companies that all take a piece of that at different layers of the stack. So I'm not sure I would be convinced that that would be zero sum.
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A
Did you buy SpaceX?
- [29:36]
B
I actually did.
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A
Okay.
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B
I don't know if I'm embarrassed or not, but I'm not going to say the amount of shares, but I wanted to be a part of the movement. So I'm on Robinhood buying my retail shares of SpaceX. I'm up like 15 bucks now per share. Per share, but so I'm happy.
- [29:58]
A
Yeah. Amazing. Well, Aaron, you know, you answered my email when we were just at the very start of this podcast, four episodes in, came on the show. I feel like every single time we talk, something crazy is happening.
- [30:11]
B
That's a guarantee at this point.
- [30:12]
A
So. Boy, are we in the thick of it, right? Yeah. Awesome. Good to see you, sir. Thank you so much, Aaron. Thank you, everybody.