Big Technology Podcast - OpenAI’s $100 Billion Funding Round, OpenClaw Acquired, AI’s Productivity Question — With Aaron Levie

Episode Date: February 20, 2026

Box CEO Aaron Levie joins for our weekly discussion of the latest tech news. We cover: 1) OpenAI's anticipated $100 billion fundraise 2) Does OpenAI's big forthcoming raise settle questions about its competitiveness 3) What's going on with OpenAI and NVIDIA? 4) Hype or True: Big Proclamations from the India AI Impact Summit 5) Why can't Sam And Dario hold hands? 6) Anthropic's powerful new model 7) OpenAI acquires OpenClaw 8) What the acquisition portends 9) If software is an API, what is software? 10) Wait, is AI not increasing productivity? --- Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice. Want a discount for Big Technology on Substack + Discord? Here’s 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016b EXCLUSIVE NordVPN Deal ➼ https://nordvpn.com/bigtech  Try it risk-free now with a 30-day money-back guarantee! Take back your personal data with Incogni! Go to incogni.com/bigtechpod and Use code bigtechpod at checkout, our code will get you 60% off on annual plans. Go check it out! Learn more about your ad choices. Visit megaphone.fm/adchoices

OpenAI is closing in on a massive $100 billion fundraise. OpenClaw is acquired as agent hype goes into overdrive. And is AI making us more productive, actually? That's coming up on a big technology podcast Friday edition with Box CEO Aaron Levy right after this. Have you been waiting for the perfect time to upgrade your tech? Good news. The wait is over. Dell Tech Day's annual sales event is here. And we're celebrating our best customers with fantastic deals on the latest piece.

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That's dell.com slash deals. Michael Lewis here. My bestselling book The Big Short tells the story of the buildup and burst of the U.S. housing market back in 2008. A decade ago, the Big Short was made into an Academy Award-winning movie, and now I'm bringing it to you for the first time. As an audiobook narrated by yours truly, the Big Short. The Big Short is made. The Big Short is made into an Academy Award. It's story, what it means to bet against the market, and who really pays for an unchecked financial system, is as relevant today as it's ever been. Get the big short now at Pushkin.fm. or wherever audiobooks are sold. Welcome to Big Technology Podcast Friday edition, where we break down the news in our traditional cool-headed and nuanced format. We have a great show for

you today. We're going to talk about the forthcoming $100 billion or thereabouts funding raise. for Open AI, where SoftBank, Amazon, Nvidia, and maybe Microsoft are expected to participate. We're also going to talk about the acquisition of OpenClaw, also by OpenAI, and some new studies about whether AI is actually helping us be more productive. Ron John Roy is out today, and we are joined by the perfect guest. Returning champion, Aaron Levy, is here with us. Aaron, Box CEO. Welcome back to the show.

Thank you. Good to be here. Never a dull moment in AI land. Seriously. So this week we have model releases. We have potential funding announcements. It's hard to figure out where to start. But let's just go with the big story. A couple of weeks ago, we foreshadowed this idea that Open AI might be on the way to a $50 billion fundraise. Guess what? It's doubled now. It looks like it might be $100 billion, soft bank with $30 billion of that. Amazon might end up investing as much as $50 billion, which is wild given their connections to Anthropic. and then, I don't know, even the numbers are even making it look like at least one 10 to me because

NVIDIA might end up. I remember these kind of numbers from our like Series A and B days. So this is part for the course. Right. Context here is that any, so Nvidia could put up $30 billion. So this is, all of these numbers would basically be larger than the entire amount raised by the biggest IPO in history. So let me just ask you this.

the narrative around open AI has been code red losing to Google commoditized, getting its ass kicked by Anthropic. Now, money is it's just numbers, it's just money. But does this size of a fundraise rebut some of that? And why do you think these companies would be making such a big bet on Open AI if some of those criticisms might be true? Well, I mean, I just take a pretty pragmatic view. to this, which is probably every fundraise after one billion, you know, the one billion market cap from Open AI, the same set of questions would have been asked. I'm sure when they were 10 billion, and 50 billion, and 100 billion and, you know, a couple hundred billion, the question was always how big

good, how big of this market possibly be. It's going to be hyper competitive. Google's going to wake up someday. There's other competition. Aren't these models going to get commoditized? So, so you have to kind of almost imagine that's always going to be the state of the the conversation that that will happen at every kind of you know juncture uh you know as we saw in the past and and i think as we will see going forward and yet at the same time um almost by every metric uh the usage of of at least opening eyes products keep growing uh certainly anthropics and geminize um and other players in the space the capability level of these models is only increasing so these models are doing more work We are still only in the earliest innings of the actual ripple of intelligence across organizations and across the enterprise.

So I think all of the metrics you just cited are relevant, but they're kind of the metrics that you would look at in the early days of cloud computing. And you're like in 2010 or 11 or 12 and you're like, wow, you know, Google just now got into the game and Azure is building up. market share and and you're looking at Amazon, you're saying, well, you know, how big now could this possibly get given how much competition there is? And I think in AI, we're kind of experiencing the same thing, which is, which is if you actually zoom out and you look at maybe the 10-year view of this market, we are looking at a really, really small percentage of the total change that is going to happen as a result of this. So we're in the earliest innings. It's crazy to think that when you're talking about $100 billion raise, like I'm, you know, I'm aware of.

of the cognitive dissonance that might exist from that. But when you're talking about just like one of the most kind of fundamental kind of, you know, core fabrics of the economy in the next century, it's just like entirely reasonable that you would both see that level of competition and you might have companies that are now approaching a trillion dollars in this category. Okay, but here's what the pushback would be. It would be that in the past, these questions have come up. You know, what is Anthropic going to do?

Is Google going to get it together? Those were ifs. Now Google has gotten it together. Gemini, I think we have a new model from Gemini 3.1 that came out this week. That is, you know, half the price of the other leading models and has about the same performance. This is a competition that has tightened in a real way. Anthropic isn't just a figment of the imagination anymore. It is dominating an enterprise.

Claude is crazy. But you just have to kind of do a slightly different math on this. You have to do everything you just said is true and doesn't, doesn't impact the valuation or funding question. We're talking about a category where, you know, it'll be measuring the tens of trillions of dollars, the market caps that will be generated by AI. some of that will go to the chip providers, some of that will go to the supply chain of the chip providers, some of that will go to the AI model providers, and some of that will go to the kind of application and deployed layer.

So if you're talking about a category that will be worth tens of trillions of dollars, you know, we're talking about little skirmishes on the path to, you know, who's going to be a $5 trillion company in this space, or a $2 trillion company in this base, or a $500 billion company in this base. So I look at it as just like the total size of the market and how that pie will likely be divided. And you can still have, you know, Google become two times bigger than they are today and have 50% of the market share from consumer traffic. And that would still support, you know, very large numbers from Open AI or Anthropic or one or two other players in the space just because of the sheer size and scale of the market we're talking about. Now, I'm looking at the size of these numbers, and one of the questions that has come up for me is, do the people... I mean, here's just for fun, just for fun.

What do you think... What do you think, if you want me to put you on the spot, what do you think the market cap of J.P. Morgan is? Let's say, $100 billion. $800 billion. $840 billion. Oh, man. I'm embarrassed. Way off. Okay. So the market cap of J.P. Morgan is $840 billion. And I'm not, I'm not. I'm not. not saying that that's a fair market cap or not a fair market cap. So no opinion on on on on on market cap. But you and I could list 15 competitors to jp. Morgan all of which I don't even know if I do anything. I don't have any jp. Morgan thing. I think I've made like a car loan or something

that's through jp. Morgan. But like I don't use japing Morgan and my daily life. And they're worth $840 billion. And if you take all of the other banks that that, you know, you just are in the you know, you're in the trillions of dollars very, very quickly across just one, one little category. Now, and so this is the, like, if you're talking about intelligence across the entire economy, you can get to, you know, pretty large numbers in a pretty reasonable way. Okay. You're setting up the question I was about to ask perfectly. Oh, maybe I didn't want to. No, I think it is. It's a great setup. You've just illustrated what I'm going to ask about the size of these numbers.

So the numbers are big. Yeah. And the question I have is, are the investors thinking that, that this is all going to be additive. Or maybe what happens is that OpenAI is getting this big because it's able to take some of that a little bit of market cap from a JP Morgan. You know, a big part of JP Morgan's business is advising clients on making investment decisions. You know, if I have a chat cheap T investment instance, you know, is that all of a sudden some of that market cap is going into the open AI market gap closer to home. We're in the middle of the SaaSpocalypse, right? Where there's this belief that AI is going to just ingest lots of what software companies are doing right now. And the market has really been unkind to software companies at the start of this year.

Very unkind. Very unfair. I feel like Trump, but like very unkind. So unfair. But on that note, like, so can you sort of describe what you might think as what happens if this is additive versus what happens if this actually is a technology that will just gobble up big swaths of the economy? Well, I kind of think about it as a multiplier on the economy or, you know, kind of a, maybe it, maybe it, you could either think about it as a force multiplier and it gets a,

it gets a tax on that or it's a, it takes a percentage of, of the economy, you know, through, through some sort of, you know, kind of, you know, labor arbitrage type pricing. but to me I kind of look at it as, you know, tens of trillions of dollars are spent on, on knowledge workers across the economy. And if you could, you know, add a 30 or 50 percent increase in productivity across all of knowledge work, could the major labs and the applications around that take a 5 percent, you know, 10 percent sort of fee on on that? That sort of, like, I think how you get to the math where revenue can get to the hundreds of billions or trillions, low trillions, and it's not like entirely unreasonable, just mathematically.

And that's, and you just are basically saying, okay, well, open AI, we'll take part of that, Anthropic takes part of that, Google takes part of that, some of the application layer takes part of that. But I think that you can, you know, there's a lot of ways you can get there, including actually just like advertising could probably get you there. Like, there's just no reason that that that your AI service is not generating 50 to 100 billion dollars, uh, just due to better performing hyper-targeted advertising as another business model. So I think, I think open AI is kind of these multiple business models stacked up that all, that all will create, you know, more and more opportunity over time. And at the same time, you know, in five

years from now, both they will be, you know, 100 times bigger an inference, Anthropic will be 100 times bigger an inference. Gemini will be a hundred times bigger in inference and so on. And that inference is more profitable, which sort of starts to answer some of these questions. The inference eventually gets more profitable. I think you're in a mode right now. And I know it sort of is, it'll sound kind of crazy and bubbly. And, you know, there's a some percentage chance that I'm totally just drinking the Kool-Aid. But I think, I think you're in a period right now where you're just in the infrastructure, build out, teach the world about AI. It's sort of worth subsidizing a lot of these use cases because it's the fastest path to figuring out where the actual value is going to be. And so while,

you know, there are some scenarios where you have a startup or a lab subsidizing tokens for coding or whatnot, it is there, it is like competitively a good move for, you know, gaining market share, getting data, building a flywheel, creating a moat, like those are all strategic things to do at this stage. Similar to how Uber, you know, had to buy their way into many markets unprofitably on a region basis. And then over time, you know, it's now a wildly profitable business because they now have obviously a very strong network effect and they're kind of locked in to these markets. And I think some of these very kind of cap-x or, you know, cash-heavy businesses up front, you know, sometimes just fundamentally require that.

Right. One note on the ads before we move on. You're talking about how ads could be a hundred, 100 plus billion dollar annual business. And let me just put a giant asterix that I've not studied that once. I'm just going off of the size of Facebook's and Google's businesses and saying there's just no reason that consumer great intelligence,

you know, that's answering any question for you wouldn't also deliver that type of business model as well. Yeah. So Open AI has gotten a lot of, I mean, so Facebook, by the way, did $60 billion in the last quarter. So this would basically, the numbers you're looking at is like half, half of that.

And the one interesting, I was speaking with an ad executive this week. And one of the interesting things about Open AIs advertising, now they've taken a lot of flack for it, maybe with good reason. But one of the interesting things about it is it's so high touch. And that's why they're charging like a $60 CPM, which is insane. It's so high touch. It really guides you through a process. It seems like it feels good to go through.

it's helpful if you're thinking about like staying somewhere. The difficult thing with advertising overtime is something that custom and that high touch has been really difficult to scale. But with AI, that opportunity to scale it presents itself. And then all of a sudden these numbers that you're talking about aren't crazy. Yeah, well, I'm on the other camp than versus a lot of people on this. I think ads can be incredibly powerful in AI products. I think that, you know, you just kind of like, you sort of have to eventually decide as a user, do you want to see products that are kind of SEO hacked?

Or do you want to see products that are kind of like marketplace economically hacked? And there's, you know, many reasons why the products that can best advertise to you might be the better product because they have the, they, they, they have a very clear financial incentive only to get you to their site if it's a good product and it works well, uh, or else you're just going to bail. And so versus at, you know, SEO, we can just load a bunch of keywords across a whole bunch of sites and create lots of Reddit posts. That's, that's all you're seeing right now. When you ask for something, you're, you're, you're seeing some form of a company, you know, you know, doing whatever it can to ensure that it's showing up inside that, that algorithm. And so it's not,

obvious to me that the marketplace model of that is is going to, you know, give you worse results. And I'm actually very, you know, I think I don't think any lab would ever change the answer that it's giving based on advertising. I think it's going to give you the answer and then it's going to give you related and recommended things from, you know, from the bidding system. And to me, that kind of makes total sense. Like, that's just like how the internet has worked for 25 years. It's funded incredible consumer surplus of products on the internet. It's why we have free search and free email and free maps. And like, there's just no reason that that would not apply to a consumer grade intelligence product as well. Definitely. No, I think it could, it's a very

interesting way of thinking about it. And you're right. You're going to get recommended products anyway in these things. So, you know, maybe, maybe that's a good signal. People want to believe that there's some kind of like, you know, amazing truth arbitrator in these systems. And they're not. I mean, they're at the exact same mercy of a prior search algorithm would have been. It's just taking signal from a variety of sources. It's doing its best to figure out what the real answer is. And if you also have a marketplace layered on top of that, it's just not, I just don't think it's the end of the world. And I think you'll actually get a lot of good recommendations along the way. And people will then pay to not see the ads. And that'll be even more revenue.

So there's just like, it's just like a very good way to make money if you're an AI company at that scale. I mean, I only think it's relevant for two or three companies, but Open AI is one of those. Yeah, they'll have a billion users or they might already have a billion now. Okay. So before we move on from the fundraising thing, there's one thing that has puzzled me throughout, and I need to ask you what your thoughts are here. So Open AI and NVIDIA announced this $100 billion funding that was going to come in from NVIDIA to open AI, $10 billion at a time.

And then it seems like Jensen was backing away from that. There was a Wall Street Journal article saying that the deal was on ice. And we found out this week from reporting from the Financial Times that NVIDIA is going to invest in Open AI, but it's going to be $30 billion and not $100 billion. Now, there were these reports that Jensen was not happy with Open AI's trajectory and all of that. And he seemed like when he was talking about it, very different from the original press releases, saying we hope they'll invite us to invest as opposed to we intend to invest. Those two very different ways of talking about it. So I'm trying to figure out, Aaron, how do I think about this? Because on one hand, they are, so this is, if this deal replaces it, that's $70 billion less. I mean, if you get $70 billion less than you anticipated, that's bad. However, they're still putting in $30 billion reportedly. That's, a lot of money. Where do you think, where do you think the relationship stands and how are we, how should we read the number and the replacement of the initial 100? Oh, I mean, this is,

this is like full astrology on. Yes, this is astrology. Yes. I mean, we're doing palm reading for, for the AI industry. I, you know, I, first of all, did, did they say that they intended to invest in the very next round or they intend to invest $100 billion at some arbitrary point in time. Yeah, I think it was overtime. It was never one round. So I don't know. I'm going to just like I'm taking all the facts that in the same way everybody else is in.

But I just don't have the impulse for the drama side of this. It's, you know, Nvidia obviously wants a very strong corporate relationship with Open AI. Open AI obviously wants to be able to be first in line for for chips. They have a lot of incentive to both make you make each other very successful. it's it's a it's a it's a it's a boon for both of them if if the whole the whole space you know keeps growing and at the same time there's probably a lot of configuration dynamics that you know that both nvita has to consider on how much to invest and that bobb and i has to consider when they think about you know their total cap table and what companies own what percentage of them so i like you know

it's a very boring answer only because i think it's like it's it's it's like fun to kind of watch the viral video and of, you know, Jensen in the street interview. But like I just might, like I kind of don't worry about it too much. I just think like this space is, is changing so quickly that I can imagine many different reasons why some configuration might end up different from, you know, where its intent was six months ago or where the lawyers decided to, you know, kind of put, put certain terms in the, in the press release. Yeah, my, my hot take here is that this is all, I think Jensen does want OpenAI to succeed. Obviously, it's them versus Google.

I think this whole thing was basically a signal from him to them. You better perform. And no more code reds. And just stay ahead. You know, my only counter take to that is I just don't think that Open AI has a challenge raising money. So I don't know that there's sort of some kind of pressure that can be exerted on them from the cap table side. Right. I think it's a bit more of a fluid market.

And it's just people looking at their capital allocation decisions, looking at valuations, looking at, you know, do you have other sources of ways of getting the capital, etc. Like, if you think about it from Nvidia's standpoint for one second, like, they don't need to own a percentage of open AI. Like, that's like, they need to sell chips to open AI. And so, and so really, they just need to ensure that they've got a very strong. relationship that is sort of very sturdy and

supporting the broad tailwinds of AI. And I don't know that there's a number that like if it turns out SoftBank wants to take more of the allocation, I'm making all of this up. But if it turns out SoftBink wants to take more of the allocation, I don't know that they're like strategically impacted by that in a meaningful way.

Because if they own more of Open AI, I don't think that that position in the cap table is going to overly sway the infrastructure decisions of Open AI. Open AI will have to make their infrastructure decisions based on just like the supply side of chips, the cost side, you know, where do they have data center capacity? Those things are going to matter more than who owns a certain percentage of their,

of their, you know, corporate structure. Like, Canada, that would be with numbers this big, there's only a certain amount of money left for them to raise. And Nvidia at $4 trillion with, you know, sizable revenues is one of the. those potential sources. I don't know. There's countries with lots of money. Yes.

We're about to see them get involved. And those places want to deploy money in future economic, you know, activities. Yes. Well, we have, we definitely have, we'll have this round, which is going to be the tech giants round. Then we'll have the Gulf State round number one, the Gulf State round number two, and then IPO. It's probably what it the way it will play out.

From your lips and God's ears. So speaking of other countries, the entire AI industry made their way to India this week for the India AI summit and some really bold statements coming out of there. So let's play a game that we play on this show every now and again called hype or true. Are these statements hype or are these statements true? We got one from Sam Altman. On our current trajectory, we believe we may only be a couple of years away from early versions of true superintelligence. If we're right, by the end of 2028, most of the world's intellectual capacity could reside inside of data centers than outside of them. What do you think?

You know, probably every one of the things you're about to say are going to be conditioned on, you know, one definition of what is the thing that is being talked about. But I think that there's kind of, that seems to be totally reasonable based on the trajectory that we're on. and I would bet that Sam has a far higher bar for for what his definition of, you know, intellectual or whatever the term was than even I would. Like I think like I think already with things like the latest round of models with the right kind of AI harness, we could squeeze out a significant portion of a valuable work from these systems with the right scaffolding and the right kind of people being involved. So I think that that is.

is a very reasonable statement based on what he's saying. That might be different than what like Yon Lacoon would say is the definition of intelligence, where where he would probably define it as can the thing drive a car, you know, with only 10 minutes of training. And I just don't, I don't have that same kind of more biological definition of intelligence. Like, like, you know, so that's why I think Sam's statement is very reasonable. Here's Dario. AI has been exponential for the last 10 years.

there are only a small number of years left for AI models surpassing the cognitive capabilities of most humans for most things. I guess that's a similar statement. Yeah, it's so true. Same answer. Yeah. Yeah. Interesting moment happened at this India summit. I'm sure you've seen it.

They have all the CEOs up there on stage and they're all, I guess, instructed for a photo to lock hands and raise their arms. And Sam and Dario, who don't seem to like each other very much, I've watched a video a couple times. Didn't it feel like maybe it was a little impromptu? Or do you think that was instructed? Is it reported that it was instructed? I don't. So I was making an assumption on the coordination of it.

Maybe it was impromptu. Maybe Modi at the middle was just like, and then everybody followed in. I saw some videos where it kind of felt like nobody really knew what to do. That's true. And they were kind of like just all figuring out because you have this moment where like Alex had to grab Sundar's hand. Yeah. And it seemed like not everybody quite knew how to coordinate this.

So so you might have maybe we just, maybe they just malfunctioned for a minute. And then by the time it was too late, it was just like, we can't, we can't hold each other's hands. So who knows? I mean, yeah, the point is, the point, we could, we could, we should, we can maybe in a future episode, play the video back and go do the play by play. But the point is everybody seemed to figure it out, except for Sam and Daria. All right. They had their hands in the air, cleansed with fists, one next to the other. Same had lobster hands.

All right, right. Yeah, they did Photoshop the claw hands on onto him. Question for you about this. Can these two guys who can't figure out a way, okay, I respect their differences. But if they can't figure out a way to hold hands for a picture, should we trust them to handle AI alignment? it's a it's a it's a that's a very uh it's a it's a very uh it's a very great meta question uh on on that um has anybody written that piece yet no i mean i that really should have been the big technology story this week i mean right right that piece i i i think it's a it's a it's a great

conundrum that we face uh that is this great little micro um you know microcosm of of of a broader issue but, yeah, I don't, I mean, you know, I pay a lot of money to get both of their takes on on the hand thing. So, you know, sometimes you get into these heated battles with a rival where people are just saying too many things in public and, and it's just like, you know, you get to this point where it's just the relationship is too dramatic and there needs to be some kind of, you know, kind of a neutral ground that brings everything back together. Maybe one would have thought India would have,

would have done that. But I have kind of full faith that we will get through, you know, hand issues and they can repair the relationship somehow. Yeah, I hope so. I mean, I think if you asked either of them right now, they would have just said, I should have just held the hand and avoid it was something. Because that became the meme out of the whole thing.

I don't think they meant for that to be the takeaway from the subject. So they had like 20-minute speeches about the, and yeah, I don't think the hand was meant to be the takeaway. It is funny, how you get all these AI leaders together and sometimes there's just one great meme. There's that. There's Dario and Demis on the small couch, which is one of my favorites. And that's a content. So very interesting development actually on the model front. We hinted at it before.

Anthropic has a new big model, Sonnet 4.6. and you've said that it is a major upgrade over the most recent model, 4.5. We usually expect these single-digit models to be incremental updates. But the stats that you shared on your evaluation for complex work are pretty significant where there's been a 15% percentage point jump in performance and accuracy, you know, between 4.5 and 4.6. This is from you on Twitter or X, shall we say. In the public sector, you saw a jump from 77 to 88%

an accuracy for complex tasks. Healthcare saw a jump from 60 to 78% and legal saw a jump from 57 to 69% accuracy on the complex tasks. That's pretty big. It seems like this model has almost been underhyped. Can you talk a little bit about these jumps and what the significance is? I think probably the main takeaway should,

be that the progress of these meaningful jumps that we've been seeing in AI coding over the past couple of years where, you know, the model at best could do a couple lines of code, you know, in a kind of type ahead type format two, two and a half years ago in coding space. And now, obviously, people are giving the model a task of, you know, write me tens of thousands of lines of code for a full project. And, and we've, just seeing this incredible rate of progress and this march up toward, you know, more and more capability over time with encoding, I think that same trend is going to come to other, other now fields of knowledge work. And so, so this jump in Sonn its model from four, five,

to four six, I think represents an example of what happens when these models just get trained across more areas of knowledge work. What happens when they are getting better and better at reasoning capabilities that go beyond coding, what happens when they get better at using tools and deciding when to use tools. And that's what our complex work eval, you know, is meant to represent is sort of how does it think through a problem? How does it decide it's got the right answer? How does it check its work? And these models are getting much better at being able to deliver on that. So I think that'll be the trend for the next couple of years. And even for our own eval, I think we're looking at one of the earliest phases of a knowledge worker type e-val.

I think we're going to have to make it harder and harder to better represent the capabilities of these models soon. But yeah, these jumps are obviously, you know, very eye-opening. You know, we're going to get a little bit into how AI will do work in the second half when we come back. But one of the interesting things that's been happening around Claude is there's been this drama between Anthropic and the Pentagon about its use. of Claude and this like, the Pentagon's use of cloud and there was this story that came out that apparently the Pentagon used Claude in its to coordinate its attack on Venezuela.

This is from ex-user Tony Shevlin. Such a compliment to Claude that amid rumors it was used in a helicopter extraction of the Venezuelan president. Nobody's even asking, wait, how can Claude help with that? It's just, of course, of course it was useful. How would you not have used Clod?

It is actually a very funny, like, two years ago, that sentence would have been like, excuse me, what would you, like, how would this have, like, what would the thing have been? And now it's just like, yeah, I'm sure they use some kind of intelligence to plan something or figure something out or, you know, correlate data. And that's just sort of priced into, I think, more and more complex work and software. Wild. Okay. So we still have so much to talk about. We have OpenClawe. We have these new studies on AI product.

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Shopify.com slash big text. And we're back here on Big Technology podcast with Box CEO. Aaron Levy. Aaron's always great to have you here. And I think you're really going to enjoy this next segment because this is something that you've been following very closely. And it's going to be great to get your perspective on it. When OpenClaw sold to OpenAI, I said, we got to get Aaron on the show for his perspective

on this. So this is from CNBC. OpenClaw creator Peter Steinberger joins. Open AI, the creator of the viral AI agent OpenClaught is joining OpenAI and the service will live in a foundation as an open source project that OpenAI will continue to support. Sam Alton said. He said that Steinberger is going to join OpenAI to drive the next generation of personal agents. So we'd love to get your perspective here just on a little bit about very briefly what OpenClaught is because it's always good to sort of refresh there. And then why is it significant that OpenAI either acquired it or brought Steinberger aboard?

Yeah. So I think the innovation that Steinberger kind of created with OpenClaught was, and there's been various attempts at this, you know, obviously over the past couple of years, but I think it was only really possible in probably the last couple of months of model capability. But the big jump is, you know, we have these agents that effectively act on behalf of us, and we are controlling it and steering it to go do tasks for us. So Claude code, you kind of type in your terminal, you tell it to generate some code, and it goes off and does work and comes back and it's waiting for its next task for you to give it.

Or codex, you're in a UI telling it to go and, and generate some code for you, Devon, factory, you know, all these kind of agents. And that's basically been the state of the art of agents for the past, you know, year or so plus or minus. And OpenClaw kind of took, you know, many of the same principles, but said, well, what if that agent is sort of running on its own? And it had access to your computer and your browser and all the services that you use. And it's just literally running on an ongoing basis.

and you chat with it and you can ask it to do things, but it can also ping you as sort of relevant. And that was this, this is sort of a very new kind of way to think about agents that again, we've seen examples of, but nothing obviously that has taken off at the level that OpenClaught did. And it gives you a little bit of a peek into what the future, you know, could be where you don't have these agents that you only sort of spin up and spin down as you need them to do work for you, but you have actually an agent that's sort of always

on kind of working for you and executing tasks for you. And that's why people are setting up, you know, their own separate computers for these agents. They can just keep running off in their own environment. And, you know, hard to know exactly how you fully would package that up and how it could manifest in a way that would be really, really simple for people to use and fully secure, safe and secure for people that don't kind of know. know their way around all these systems. Lots to figure out there, but, but not that different from, you know, what I, when I think about it as like a, you know, a principal update or a paradigm update, you know, I remember the viral

video of, of, of Devon must be two years ago now. And, you know, I don't remember exactly all the details if they, if they did a Slack message or if they were in the UI, but you, you kind of told Devin to go off and do work and you could just see it, it's producing its code. it had another environment where you could see what it was building. And, you know, they got, they got, you know, I think there are a lot of people that were like, oh, this will never work. How could this possibly work? It's not actually doing that.

And there were these viral takedowns from non-believers. But for some people who were deep in the AI space, we were like, oh, shoot, like, that is a very different way to think about, you know, working with an agent. You're not in an IDE. You're not coding alongside it. You're just setting off a task. and it's going to go and do a bunch of work for you. And now, obviously, it's very clear that that's the dominant paradigm that we're going to be in.

Codex has proven it. You know, Claude Code has proven it. Devon and Factory have proven it. You know, I assume Curser is betting even more on agents. You can kind of see them pushing more on the agent's side of the user experience as opposed to the IDE side. So that was an update that we got a couple of years ago. And I think we're going to see the same thing now in other. other areas of knowledge work and open and you know open claw introduces an interesting kind of

paradigm that that could that could persist across you know more and more areas of work right now as a software CEO I really would love to hear your perspective on what this means for software I'll just give some context here you know I've spent the past I guess week and a half now just like with my nose in cloud code I've just been going crazy with it and you know initially it was like Can you build me like a basically a software version of a spreadsheet that like sends an email when I complete a field? But then it was like, well, why don't you plug that into YouTube's API? Why don't you plug that into, you know, I'm right. I'm looking for an apartment.

Can you plug into Street Easy and Zillow? And all of a sudden it's like, oh, it goes from basically me going to the internet to the AI, you know, sorting through the internet for me. And you actually tweeted about this with the open clause situation. You said, in a world of OpenClaw Codex, ClaudeCode, Co-Work, Manus, which meta-acquired, and other agentic systems, it's becoming clear that the future of software has to be API first, but also enable human interaction for verification, collaboration with agents and people, and working on the output. So what does it mean for the software industry if it becomes API first? Because, you know, on one hand, you're enabling your customers to get rent this amount of utility if they're interacting with you. this way. On the other hand, you know, Zillow probably got some value and me going there. YouTube probably wants me on YouTube. Now it's all happening in my like, you know,

my dashboards that I've built with cloud code. Yeah. So maybe we'll separate the markets a little bit because you threw in a lot of consumer products at the end of that. You know, I, hard to say how much, how much of the consumer internet kind of gets collapsed into API calls versus, you know, the average consumer just still wants to go to YouTube and see the feed and they're not going to do the- For me, YouTube is, that's strictly on like the back end. So that's like the creator side of YouTube. Like I used it to sort like thumbnails and then rank them by, you know, click-through rate and then also tell us how long people are staying on the videos. But I have a point taken on the consumer side.

You're not going to want to go to your Claudebot to watch YouTube probably. Yeah. And so that's why I kind of separate a little bit now. Now, I'm, but you have to be a little bit sympathetic or at least think through because, because again, absolutely major consumer properties are going to see a reduction in traffic when the answer just comes up in chat to BT or when, you know, some kind of automated system is just delivering the answer. So, so I think that's a whole, whole category people have to think through.

On the enterprise software side, that's obviously where we spend our time. We, I'll speak for Box for a second and then maybe you can broaden it out for software. at Box, we're like 100% excited about this because the, you know, one of the things that agents are both really good at, but also need for their workflows are your files. They need to be able to access the information to work with to answer questions for you, to produce new information, to be able to store off memories and it's working and their working sessions that you can go and interact with. they need to be able to read specifications and documentation, all of that ends up being files. So what we are building is a platform layer that whether you're a person interacting with your data, whether you're an application that needs to access data, or whether you're an agent that needs a file system to interact with, we want to be the platform layer that connects all of that.

And the key, at Box, we think we're in a kind of unique position is we don't, don't think it's enough for the agent just to have its own sort of sandbox environment of a file system, nor is it going to work for just people to have a separate environment. You're going to need something that actually connects those two worlds together. So that people are going to need some form of end user interface, even if that's an end user interface in a chat bot, they're still going to need to kind of interact with their data with something visual. And they'll likely eventually want to like log into something and see all their content and be able to manage their sharing, permissions and who they're working with. But agents just need a set of APIs. And agents need to be

able to work with those APIs and facilitate all of the work that they're doing. So what we're investing in is making sure we've got the most powerful capabilities for agents to be able to work and work with all of this content that you want to give it. Now, there's all these new implications, which is how do you give an agent a separate space to work in that you're collaborating with that agent, but its blast radius is somewhat contained, so it doesn't kind of delete all of your data. And now all of a sudden you have this kind of crisis on your hands because your open claw agent went and mucked with everything. That just happened to Amazon, by the way.

I mean, not to interrupt you, but Amazon, there was just the story in the FT that Amazon had lots of had outages because the agent was like, you know what I'm going to do to fix this problem just erase everything. I can make the problem go away. No more code. Delete. You didn't like your... Always good solution.

You didn't like your folder structure? Great. Now there is none. So you do have to, you have to be thoughtful about how do you kind of create the right lines of demarcation between these systems. But again, for us, if you imagine that there's five or 10 or 100 times more agents in the future than people, which is, I think, a relatively safe assumption given the productivity increase that they're going to enable, all of those agents are going to work with enterprise information. They're going to need a secure space to work with that information. They're going to be able to store that data. They're going to be able to store that data. They're going to be be able to operate off of it. They're going to be able to answer questions for end users. They're going to be able to, you know, need to be able to store their own data. So that's what we're building. And we have to make sure, again, we make that as easy as possible for agents to go and utilize. I think that there's a meaningful amount of software that already exists that will also have to do the same thing. They will have to make their software ready for agents. I think there'll be some forms of software that get kind of compressed where agents don't really need to use their tools in the same way that people did. And that's obviously

where you're going to see some pressure in the software market in some areas. And then there's going to be all new platforms that have to exist because we didn't anticipate the kind of new problems that agents are going to run into. And that's where you'll have, again, API first companies get launched from the start thinking only in terms of platforms. And I think this is just going to be, you know, a tremendous amount of growth for anyone who at least has a play in that architecture. Okay.

So you mentioned productivity. And I think this is something that's worth example. as we end the show because I think there is this sort of discussion around AI. Oftentimes it's while there's productivity increases and it's sort of accepted like, you know, that's that there already are or there will be. But the data is a little bit mixed and I just want to run it by you and get your perspective on what the data is saying. So this is from Fortune. Thousands of CEOs just admitted AI had no impact on employment or productivity.

And it has economists resurrecting a paradox from 40 years ago. So it talks a little bit about how in the 1960s, we had transistors, microprocessors, and microprocessors, integrated circuits. And productivity growth actually ended up slowing from 2.9% beforehand to 1.1% in 1973. And so now you have all the CEOs that have been pulled. And it is, yes, 6,000 CEOs. Two-thirds of the executives reported using AI, but it was 1.5 hours a week, 25% of the respondents reported not using it in the workplace at all.

Nearly 90% of the firm said AI had no impact on employment or productivity over the last three years. I mean, maybe this is research done last year, but even still, you know, I'm curious. Actually, I'm curious. When was that published? Or when was the research taken? It is published February 2026. I don't know exactly when the research was conducted over. Yeah.

Yeah. But with the number of respondents, obviously, that would have been probably, you know, sometime last year. But sorry, keep going. No, go ahead. Oh, like, I didn't just like, like defend AI or what? I was just going to ask like what your perspective is here because it does seem like we're, you know, in some ways, and this is sort of, we want to pressure test a little bit about like some of these assumptions that we're going to have more AI agents than we'll have workers, that it will lead to this increase in productivity, whereas we're still seeing data where that is at the sort of best when you look at this data up in the air.

Yeah. Yeah. I can understand the dissonance that might be out there between the tech enabled economy and the rest of the economy because what's happening is in tech, these agents are so effective at coding. And developers have far fewer barriers. to adopt agents for coding, then the rest of the knowledge worker economy has for the same level of productivity gain kind of use cases. So in coding, you've got these just incredible

properties, which is the models are hyper trained on code. They, you know, coding itself is a, is a text only medium. You know, Dario and Dwar Keshe on their latest podcast kind of hinted at an interesting point, which is your code base contains most of the, the context that you end up working with. It's got your documentation. It's got your all of the existing work that you've done. And if you kind of compare and then you developers are just, you know, are obviously more technical, generally more tapped into the internet and what's going on and the latest trends. They pull down the latest new products and, and try them out. Now you can compare that to the rest of knowledge work, you know, the marketer at a CPG company,

the, the, you know, lawyer in a mid-sized law firm, you know, I'm making up, you know, some kind of characters of, you know, various job functions. But basically, like, they're going about their day and they're not thinking, like, how do I go and construct my workflow to just fully take advantage of agents and automate everything I'm doing? That's just, like, probably not top of mind for, you know, most knowledge workers. They're going to go to chat to BT. They're going to ask some questions.

they're going to get an email written for them. They're going to summarize a, you know, a document. They're going to build a new strategy plan. And then, you know, they're going to be, you know, the company will do incrementally a little bit more as a result of that. And maybe their strategy changes a little bit more or the financial analyst comes up with some new insights. That's, I think, probably been the state of AI for the past couple of years,

at least whenever a survey like this would have tried to analyze. Compare that to engineering, where, You know, we have products that we build five, you know, these are the estimates from the actual engineer that we will build five times faster because of AI coding. And we will, as a result of that, be able to ship significantly more capabilities to our customers. We will be able to solve significantly more problems for our customers. In many cases, we might not even charge more for that functionality.

We are going to pack that into their existing licenses because we now can. So to some extent, what would you measure in our kind of productivity? This is now just a priced in thing that we do because we have to deliver more and more value because obviously tech is hyper competitive and we want to now add more capability to our customers. I think that has not yet rippled through the rest of knowledge work. And I think it just will. It will have to because the tools will get better and better.

And you'll have one competitor in a market that is able to use. use AI to either lower their costs or lower their fees to the customer or be able to deliver a substantially higher product to the customer. And as you see more and more examples of that, that will just start to transform these market dynamics. You know, I would say equally that, you know, I like to operate off of, you know, I think Bayses had this line is when the anecdotes and the data disagree. You have to look at the anecdotes.

And so, you know, look at the, you know, the equal headline from two weeks ago of KPMG asking their auditor to lower their fees because of AI. That, I think, is your, that's your initial signal of actually what's going to happen, which is a company is going to say, you know, that kind of work that that we now know we can, we can bring automation to. We should be spending less on and then using those dollars to do something else in our company that is. that is higher productivity or more, or that makes us more effective or more competitive. And once you do that dozens or hundreds or thousands or tens of thousands of times in an ecosystem, that's where you'll start to see kind of this reshaping of how these markets will play out. It's happening in tech, unquestionably. And now the only thing is what's the roadmap to that happening across the rest of the economy?

That's going to take time. People have to change their workflows. People don't have data set up in a way that is sort of prepared for, agents. The agents themselves don't always have the right interfaces or tooling to be supported in knowledge work. So, so I'm actually extremely pragmatic about this where I think I could agree with the survey that you just read and equally be completely unfazed and, and more of anything, just say people should be probably prepared for this will come for more areas of knowledge work. I'm the biggest optimist on the jobs impact of that. So I don't see that as a scary thing. I think

it's just going to mean companies will have to sign up to do way more for their customers. I think that that will be where it shows up is, is, is we will have a surplus on the consumer side of all of the vendors that we work with. We'll just have to deliver better and better services for us. Or if you're a B2B company, then all of your vendors will have to deliver greater services. And, and we will wake up in five or 10 years. And it'll actually kind of feel like relatively normal. Like, like, there's not going to be some kind of crazy, it's not going to be the sci-fi movie.

It's going to be that, that we just, we just have increasable. mentally better consumer experiences and better services, just as if you went back, you know, 40 years ago and tried to imagine life of, of a lawyer or a healthcare professional. And you'd be like, wow, how did you do your job? Like, without a computer? Like, how did you, like, how did you understand the legal case precedence without an internet search that you could go do? Like, that's going to be work in five years from now is you'll be like, how did you do that without an agent that drafted your entire contract, you know, for you instantly so you could respond to the client that was on the phone. That, that we will have that same set of questions and be confused how we even work the way we do

today. But yet it won't be some kind of, you know, completely transformation of, you know, we'll stop people, they'll be working together, they'll deploy tasks to agents. Those agents will go off and farm and do work and then people will go and bring it back to the, to the task at hand to move whatever their their sort of, you know, work or project is forward. That's right. Yeah. When I'm watching Claude Code go, I look at it and I say, wait, people did this before. It seems like a lot of time to do things that are automatable.

No, but literally like we, you used to have to spend like two weeks on like a library, you know, change that you wanted to make in your code base. And that's now a 10 minute activity. And but, but are we spending any less time building software? no. It's because we're just now doing the things that we didn't get to because we were spending the two weeks doing the library update. Right. Okay, Aaron, we have to get you out of here because you have to go to your next meeting, I think. But I just want to say thank you again. Always great having you on the show. Next Wednesday, we're going to have Michael Pollan on. He is the author of a new

book about consciousness. So we'll talk about AI consciousness. All right, everybody, stay tuned for that. And we'll see you next time on Big Technology Podcast.