B (3:09)

It's funny because I think the takeaway is one of the oldest sort of structecary axioms that I wrote about for years. And it, you know, it took me a while to come to it, a year or two. And I think the company that I always think about is Dropbox, where Dropbox was an incredible product. Like, I don't know, maybe you're too young for this to remember sort of back in the day, I don't know if you're still running around in elementary school, you know, your football jerseys or whatever.

A (3:41)

What I can say is I've come to love Dropbox over the last four years working at Certecary.

B (3:46)

Well, so back when Dropbox was a thing, I remember I sort of blew everyone's mind at business school where people were still using USB thumb drives to like get files around. And I think I was at some sort of stupid networking event. And then I needed like, I needed something like a resume or something or whatever it might be. And I just went to a computer, logged into Dropbox, got what I needed, it was there. I think I might have even changed it slightly to like be tuned to whoever I was giving it to, whatever, like all this dumb stuff. But this idea that it's like a USB drive in the sky, you can have your applications anywhere at any time, whenever you need it. And Dropbox had come out, I think fairly well before then, but I was a super. Actually it had been out for several years at this point, I believe. And I actually used it at this school I was working for some English schools in Taiwan and. And they had these massive whiteboards where you would write out these sentences and for drilling and going over as a class and all, like literally there would be like 10 minutes where people would just sit there and the teacher would be writing out all this sort of

A (4:55)

thing, scrolling out the lesson on the whiteboard. Okay.

B (4:58)

And so what I did is I took this whole Curriculum and I put it into Keynote but then put Mac Minis in every classroom. There was like multiple locations. So it was spread out all over Taipei and a couple locations outside of that. And the question is, how do you then actually keep all those in sync? So what I did was there was like a master Mac Mini and then I wrote a couple of Apple scripts that would basically push stuff out so you could do stuff on the Mac Mini there. It would push it into a dedicated Dropbox folder that synced with all these Mac Minis all over. And then each Mac Mini had like a cron job that ran at night that would take everything in the Dropbox folder and push it into the actual folder that was used for teaching. And the point of this was you didn't want teachers like randomly be able to like push back and like undo everything. But the long and short of it is I built this sinking, quote unquote sinking engine that was not me at all. It was just Dropbox, which is like this technology that was like amazing and solved all these.

A (5:55)

Don't sell yourself short. It was a little bit you and a lot Dropbox saving 10 minutes in classrooms all over Taipei and beyond.

B (6:03)

Well, the point though, it was more than that because you had to do it multiple times a lesson. But the big thing was it was so easy to use. It was a folder in the sky. You put stuff in the folder and if it was in that folder, it would be everywhere you were signed into Dropbox. And that could be other computers or it could be you log in through a web browser, all that sort of thing. And for you know, I very early on have always been like the highest Dropbox tier. I keep all my files there. I always wanted it to be. And it's still the. I haven't tested this in a while. I used to test it like fairly regularly to make sure it worked. But I should be able to have lose my computer or have it, you know, fall in the bottom of a lake or in San Francisco, get my car broken into and stolen or whatever it might be. And I should be able to walk into a Best Buy or an Apple Store, buy another computer or even go into like, I don't. Do they still have. What are they called?

A (6:55)

Like Internet Cafes?

B (6:56)

Yeah, I mean like so and I should be able to do my work. And the. And I was like pretty. Pretty disciplined about that. Again, like I said, I've probably sort of slacked off around making that all still works, but I'm pretty confident it still will. There's actually lots it's gotten more and more challenging for security reasons because everything you need two factor authentication. You need like a core piece that you can sign into so that you can get all the stuff you need to sign into everything else.

A (7:27)

If your backpack gets stolen, it might be a problem logging into Dropbox.

B (7:32)

Right. Well, what if you don't have your phone?

A (7:33)

The big thing is if your phone is in your backpack along with a laptop.

B (7:37)

Right. Well, this gets into. I carry backup phones. But even if you lose your backup phone, then what do you do?

A (7:42)

But we're all over the country and we're working from the same base, working from the same documents that are edited instantaneously anytime any one of us does anything to them. All of which is made possible by Dropbox.

B (7:55)

Yeah, well, I mean it's all, that's all sort of table stakes actually. We use a, we use a hodgepodge of stuff. We all, I mean like our documents.

A (8:01)

SharePoint.

B (8:01)

Yeah, but anyhow, it's commonplace now. It's totally normal table stakes. You expect stuff to sink. You know, Apple has it built in now with icloud. But back, you know, 15 years ago, I guess more than 15, 17, 18 years ago, it was a new thing and it was awesome. And Dropbox really appealed and blew up in the consumer space. And they had a great viral bit which was if you got someone else to sign up, that you got more storage, which was like. So the. And but what happened to Dropbox? And they tried to build these consumer features, like some of this photo stuff. They had this whole carousel thing, all these things. What they ultimately had to do is they had to sit down and they had to completely rewrite their application and basically be dead in the water for a couple of years. And they had to rewrite their application so it'd be suitable for enterprise. That means like all these permissions and like, you know, like a much more tying into all the, you know, enterprise auth standards, all these bits and pieces. And the reason is that while people like me, nerds, was happy to pay for Dropbox, consumers don't pay for apps. They don't pay for productivity apps. And productivity apps has been something I've cared about for ages and ages. It's the reason I like the Mac, even back when Mac sucked, because it blew people's mind. I went to Microsoft and I had to use Windows and people were like, what do you miss about a Mac? I'm like, I miss third party applications. Like what I thought the whole thing is Windows had all 30 applications. Well, Windows had All these applications that enterprises would write like line of business apps, but those went away to be replaced by SaaS apps, by and large. And what the Mac always had was like this whole ecosystem of small developers that would do all these bespoke apps that were super useful and those just didn't exist or didn't exist to a similar quality on Windows. And I love all that stuff. I love productivity apps. I love like and you know, and I wrote a lot in the App Store about how Apple wasn't enabling a business model for productivity apps for a long time. The App Store was, you bought an app once and that was it. And I wrote tons of articles. You need to support subscriptions, you need to support upgrade pricing. They still don't support upgrade pricing, which irks me, but at least they support subscriptions for apps now, which is a much. One of my earliest articles was explaining why subscription pricing is good for productivity apps. You're getting ongoing utility from it. You want it to get better. That's matching up your payment for what you get. That's all right. And consumers hate it, right? Like consumers more and more will pay subscription fees for us, but they don't like it. It has to be really important to them. They're still going to complain all along the way. Meanwhile, the enterprise has been paying like Microsoft shifted to a subscription pricing ages ago, like around circa 2000, maybe late 90s. This was the thing that Steve Ballmer executed that was actually really impressive. He obviously gets lots of critique and complaints. But the shifting Microsoft to being a subscription business was genuinely groundbreaking. It aligned the delivery of value with the capture of value in a way that made if you're a logical spreadsheet driven CTO in an enterprise, it's like, yes, I'm getting value from this on an ongoing basis. I'm happy to pay for an ongoing basis. I have support lined up. There's a security story, there's gonna be immediate updates. Like it's just a good fit for productivity subscriptions. It all goes together. But you have to be like hyper rational to appreciate how good it is. And enterprises can be hyper rational about that. And they're paying employees. So if they can make those employees more productive, it's well worth the price. And consumers aren't right. Well.

A (11:42)

And OpenAI. I mean they've been working with a subscription model chasing the consumer market for the last three years here.

B (11:49)

And that's exactly right.

A (11:51)

Be working with a subscription model. The target should be enterprises. And I think they're now clear on that point.

B (11:58)

Right so what happened in the consumer space is consumers were fine with ads and they didn't really care about their data. Whatever. Enterprises obviously care a lot about their data. They don't want an ad supported model of the enterprise. They want to pay. Well, consumers pay. They just pay with their attention and, and you capture that via advertising. And so you have this great bifurcation where on the enterprise side you pay with subscriptions and on the consumer side you pay with ads. That's the business model that sort of works by and large. And it's weird, like Sam Altman's been in Silicon Valley forever. A lot of people, you know, involved in OpenAI have been in tech for a long time and it's always like, it's like a reminder. This is why Shachekery has a business because no one else thinks about this stuff as much as I do. That they were like convinced for ages that this sort of theory of the case. And I'm pretty sure Sam Altman told me in an interview sometime I can sort of maybe go dig it up. But that like AI is such a boon, it makes you so much more productive that we will basically defeat gravity. Yeah, that's right. And consumers will subscribe. And I'm like, no they won't. Like they're just not going to. You're not going to get. Yes, it's impressive how much many people subscribe to ChatGPT. It's impressive how much revenue OpenAI makes from what was by and large consumer focused subscriptions for a long time. But you're going to hit a ceiling because the fact of the matter is most people aren't going to pay. And most people, consumers, they don't want to be productive, they want to be entertained. Right. Like an aspect of at work. Even at work they don't ne. This has always been a question which we'll get to in a little bit, but there's been a ceiling, I think on chatbots in the enterprise because chatbots entail people actively using them. Like and yes, some people love their jobs and they want to do it better and be more productive. You're going to hit a ceiling as to how many people are like that. Right.

A (14:06)

Some people just punch in and punch out, you know, punch in and punch out.

B (14:09)

If anything they're annoyed at having to be more productive. Right. Like there's lots of interesting studies.

A (14:15)

Oh there are, I mean there are millions of people who are annoyed by the top down edicts to integrate AI into their daily workflow. And I've got some friends in finance who have like quarterly meetings where they have to explain how they're using AI in their job every week. Not everyone loves that. Let me tell you. Anecdotally, at least it's interesting.

B (14:36)

I didn't even think about this until now, but this is something that you weren't. You spent a lot of time at business school about and which seems like a waste. You know, everyone at tech hates business school. People from business school and understandably so. I mostly hate them too. I understand like, but the like there's a lot. There's so much about organizational design, KPIs like incentives, how you compensate people. Cause that's actually, it's funny because you know, business school gets looked down on as, you know, how much work are you actually doing? Which is true. Not that like there's a lot of quote unquote. Some of the trips generally entails a lot of drinks take. Right. I mean like I feel compelled to make sure my kids know how to ski. Yes.

A (15:19)

Yeah.

B (15:20)

Because going on the ski trip is super important in business school. Right.

A (15:25)

Like what a bunch of nonsense. I say this is someone married to an mba, but. Yes, continue.

B (15:31)

But actually this specific bit is the hardest part of business. And it's a part that tech people, you know, particularly people who are very sort of entrepreneurial and very sort of love computers and they're always trying to figure out how to do new stuff on computers because it's fun and interesting. Have like no theory of mind for

A (15:51)

which is bit being coordinating across entire organizations.

B (15:56)

Incentivizing. Like how do you get a large organization of hundreds, thousands. Tens of thousands. Hundreds of thousands. Some companies like Amazon, millions.

A (16:09)

Yeah.

B (16:10)

How do you get everyone rowing, broadly speaking, in the same direction and doing the things that you want done when the vast majority of people in your organization are just there to collect a check because they need money. Right. And it's a fascinating. That's why you have orgs people like why they reorg like every few years. Right. Or they change stuff around. Because what happens is you design an incentive structure and an organizational structure and you set certain KPIs and everyone ends up optimized to their local maxima. They will maximize their KPI. Right. So in Microsoft, there's clearly been a KPI about integrating copilot. What happens is you get copilot everywhere in places you don't want it and it doesn't work very well. But that's what they're incentivized to sort of do. Right. And so Microsoft I think just had like Another, there was some sort of reorg going on. It's like you don't approach the organizational goal directly. You approach it in a zigzag pattern where you're kind of going in the right direction with your KPI instructor. And then you're like, wait, we're going way off board now. Time to reorg. Time to re. Incentivize.

A (17:20)

And then use.

B (17:21)

Turned.

A (17:22)

Use.

B (17:22)

You turn to the right and you're kind of going in the right direction. And then you go past it and you're going somewhere else. And then you have to do it again. And it's easy on the outside to look at this and to think like, these stupid business people, what are they doing? Like, why can't they just get it right the first time? Because they're dealing with people. And people are a lot more difficult to deal with than computers. That is sort of the nerd fallacy, which we've sort of talked about this in the context of AI replacing jobs and all these sorts of things. To understand and deal with a computer is exceptionally difficult. And the number of people can do it well is quite small. Yeah. But at the end of the day, if you can master it, the computer does what you tell it to. Like, the thing about bugs is bugs. There's a very small number of situations where the bug is not the fault of someone in the programming stack. Like there was a famous calculation bug in intel processors, I think, in the 90s, where it would actually make the wrong calculation. Like it was this big thing that it's a bug technically because it was in the design of the actual chip itself. But by and large, a quote unquote bug is you told the computer to do the wrong thing. You didn't realize you were telling the computer the wrong thing. The computer did exactly what you told it to, which is that your instructions were flawed.

A (18:47)

Well, and you can easily identify a bug and address it.

B (18:50)

Well, easily in quotes. But yes, you can, at least in theory, easily identify the bug.

A (18:54)

More easily identify a bug than easily identify a cultural headwind that you're dealing with across an organization.

B (19:01)

Well, just. Yeah, you're dealing with people. And dealing and managing people in large organizations is exceptionally difficult. There's a reason why there's entire disciplines, entire schools that are devoted to this question and which everyone looks at says, wow, you seem to do a really bad job because, like, no one, you don't. You seem to be producing graduates that are changing all the time and don't know what they're doing is. That's a Big. That's a, that's because they're trying to manage humans.

A (19:30)

There's a reason that people go to business school. I don't know if I believe you,

B (19:35)

but yeah, maybe not. I mean they both go to business school to get a job. But yes, but no but, but this is a, a huge amount of business school is. You know I've talked about. I got a lot of value like a lot of like the game theory classes and accounting. Actually I was annoyed. I take these accounting classes actually. Number one, accounting is super interesting. I really liked it. And number two, it's obviously been exceptionally valuable these days. Right. One of the big sort of this is a bubble arguments has come about the question of depreciation. Right. Like how many years are these GPUs being depreciated over? Actually they're only good for a couple of years. These companies losses are actually going to be much larger than they're showing on their books. XYZ Turns out that doesn't appear to be the case. The price of old GPUs is going up. So like they're actually companies are making more money on old GPUs including ones that are depreciated. So which I think is. But the question of depreciation and like all those sorts of things. Accounting sort of questions and like how do you actually represent the business? And this is actually, I don't get into this too much but the question of like cash flow versus your accounting like because especially with depreciation comes in that depreciation money goes out the door right away and then your only account for it over time. So you need to look at cash flow. On the other hand, questions of debt, right? Like it's funny because everyone is scandalized about Amazon projecting more capex than free cash flow.

A (21:00)

Yeah.

B (21:00)

And it's like, oh, how do they do that? They issue debt. That's the, that's like for most normal companies they actually carry a significant amount of debt precisely because debt matches capital investment. Capital investment. You put a lot of money in up front and you reap the gains over time. What is debt? You get a lot of money up front and you pay it back over time. Right. There's a reason like debt is not a bad thing. It's a incredibly useful and important tool. Right. And actually one of the funny things about tech is how little debt tech companies have traditionally carried. And this is actually a bad thing not just because it matches up the cash flows, but our tax code dramatically favors and gives you real benefits for using debt. So there's actually a bit where all these companies have been significantly under monetized by not having the correct capital structure, like the amount of debt they would carry. I wrote about this like a decade ago when Apple first started issuing debt. It's like, why is Apple issuing debt? And it's like, well because Apple's carrying. You think about it from a consumer mindspect, like wasn't it good that Apple has hundreds of billions of dollars or whatever in the bank? It's like no, that's actually, it's very suboptimal. Right. So that's the other thing about like the fact that every company is spending up to their free cash flow, which basically if you wanted to predict how much are the tech companies going to spend on CapEx this year, you could just look at what their projected free cash flow was and that basically that's what meta spending, that number, that's what Google's spending at Microsoft is under it a bit, which we can talk to. Amazon is over it a bit, but that's the ballpark. But the reality is they can all go much further. They could spend way more if they start getting into debt like or if

A (22:43)

it got really bubbly they would all be over levered on the infrastructure.

B (22:48)

Right. The one that's really in debt or taking all that is Oracle. Right? But Oracle's earnings were incredible this year and like they're, they're their revenue rpo, revenue purchase obligation, like basically the amount of committed business they have. So you have to put on your book as a liability because it's like we've agreed to serve this, like, but it's like it increased like 50 billion or something like that. Like just the numbers are nuts. Anyhow, I'm way off base.

A (23:17)

I know. We're going back to OpenAI now.

B (23:21)

Thank you for, thank you for throwing in, you know, a rope to me as I've doubted this.

A (23:25)

No, but it's been terrific. We got a free business school class there at the top of the show.

B (23:30)

So OpenAI's problem is. Yeah, they were predicated on a business model that history says doesn't work.

A (23:36)

Well, hold on, let me read this email from Adrian because I am talking to the author of the Accidental Consumer Tech company here with OpenAI and Adrian says OpenAI should not pivot to coding an enterprise. This is a terrible pivot unless you think Google can't be beaten in the consumer market. The scale that OpenAI could achieve with a consumer platform creates a flywheel that you just can't achieve in the smaller, more competitive enterprise market. Professional applications of AI are the most interesting right now, but that is bound to change as one, general purpose AI models improve two, smartphones, watches, glasses, jewelry, etc improve as AI interaction points and three, consumer adoption increases. The Internet's early adopters were government and academia, but so much of the value created on the Internet has ultimately come from everyday consumer use cases. The business model of the Internet is ads, which works because the massive audience makes for great ad targeting, which means ads actually make advertisers money and serve products that are interesting to users. Thanks for teaching me this, Ben.

B (24:46)

You're welcome.

A (24:47)

Adding value everywhere is better than adding value for large enterprises. Am I underrating the value of enterprise solutions? If so, tell me how I'm wrong. What do you think about that?

B (24:59)

So, a couple things. Number one, the OpenAI rumor, all hands meeting, whatever, didn't say they're exiting the consumer business. Right? Yeah, we know they have side quests that can get rid of. Like Sora, for example, like the Sora app. We spent a lot of time on that. We've been chastised, I think over four.

A (25:15)

It was a fun couple of weeks, but it didn't ultimately matter to the bottom line.

B (25:19)

Right. And there's some other of number. Number of other things. And from what I understand, you know, another thing to potentially point to is the hardware question, which Sam said, no, we're not getting out of it and I'm pretty sure they're not getting out of that. I think they're.

A (25:37)

He said, we are not shutting it down. Quite the opposite. I think you will love what the

B (25:41)

team is building and so that's probably the tell. Are we giving out the consumer market? And I don't think they are. And so number one, let's not overread what they said.

A (25:52)

Right. I think some of the. The reactions, not just Adrian, some of the reactions online were a bit too dramatic. Like I didn't come away thinking OpenAI is just going to punt on the consumer space altogether. It makes sense as far as focus going forward. There's a massive opportunity in the enterprise space right now on the subscription basis. It makes sense to channel more resources in that direction, particularly given how Performant Codex has been, which we discussed last

B (26:23)

week, like also where all of this anthropic is threatening to basically just win the whole thing. Right. That's what's really happening here is Anthropic's blowing up in the enterprise. They have been for a while that it looks their growth over the last two years looks like an exponential curve in that, like they're growing, they're growing, they're growing and holy crap, they're growing. You know, like from 14 billion run rate in January to 19 billion. Like now something crazy like that, like just dramatic sort of ramp up. And the issue is enterprises do pay for productivity. And if AI is indeed a massive productivity enhancer, such a massive productivity enhancer that it not only makes employees more productive, but potentially replaces employees, the size of that market is probably larger than Adrian's giving it credit for. And that may be a sufficient flywheel to then go into the consumer market. And there is an analogy for this, which is Microsoft. In the 80s and 90s, Microsoft was an enterprise company. They got the Windows flywheel going in enterprise and then basically won the consumer market for free. And Microsoft itself forgot this lesson right when they were trying to do like the Zune. And like, oh, it's like they were never a consumer company. They won the consumer company by virtue of dominating the enterprise. And then they sort of got consumer for free. And they still have hangover from that. Like, why is Microsoft still dominant in terms of gaming that's downstream from winning enterprise in the 80s and 90s, right? Like they've done investments, there's. But like they were had critical mass at the moment when gaming blew up was a big thing. Everything's built around Windows, the components and the hardware and all these bits and pieces. Like, so they still maintain consumer presence, but they've always been an enterprise company that sort of got that for free. Whereas Apple was a consumer company that was in the wilderness, it almost went bankrupt because they were trying to sell to consumers at a time when consumers weren't willing to pay for what they

A (28:31)

were charging a premium for the first 20 or 30 years.

B (28:34)

Well, they were charging a premium. And just like there weren't that many people in the consumer space that cared enough, that cared to get a computer and cared enough to buy a differentiated computer. Just easier to get what you get at work, which by the way was much cheaper because you had this flywheel going. The computers were much more inexpensive and more performant. And like just everything about the Apple thing was worse. So just to push back on Adrian, we do have an example in tech history of, of the more important flywheel being enterprise that lets you win consumer. And when we haven't fully fleshed out the business model, we know like enterprise will just pay. They'll pay for what they're getting and that's, that's super powerful and we could

A (29:12)

lose it if we don't seize the opportunity over the next year or two here.

B (29:17)

Right, well, and so this gets to the accidental consumer tech company bit where it might thesis has been. OpenAI is large. ChatGPT is massive. So you have to get into a consumer business model, which is ads. There's a bit where the sheer scale of ChatGPT is actually OpenAI's biggest problem. Like they have to support so many people and so much of the. They have so much more compute than anthropic, but that compute's all going to serve all these consumers where. Okay, so we have to model, we have to build an ad. Oh my God. Building ads is hard. Like Google's already has it built, Meta already has it built and they're accelerating their offering because they're applying AI to it and we're starting from scratch. Like there is like, in theory, yes, they should have a huge ad supported consumer business and maybe they add devices to it and all this sort of thing, but it's a long and difficult road to hoe. And yes, in theory, that is the better flywheel. The consumer market is larger than the enterprise market, generally speaking, but they need to actually make money to pay for all this sort of stuff. And making the amount like enterprise is easier. And it's probably a pretty larger market than Adrian's giving it credit for.

A (30:41)

Well, and there's a direct line of sight in terms of what OpenAI could do in the next 12 to 24 months if they seize the opportunity and go after it aggressively.

B (30:51)

And there's a bit where they just get locked out because everyone's like, sort of like standardized on anthropic. Right, Exactly.

A (30:56)

That's the risk. That's the risk that I'm sure is animating a lot of what's happening here. And it's hard because all these are private companies. And so you'll see these charts about Claude, like eating into the enterprise space and who knows what.

B (31:09)

I posted that chart from Ramp a couple weeks ago. Got some very strong pushback for open AI that, that they don't believe that that is close to being accurate.

A (31:17)

Interesting.

B (31:18)

The thing with Ramp is it's used by a lot of startups and like very tech, like these Silicon Valley products are used first and foremost by other Silicon Valley products. And like Ramp by all accounts is doing extremely well and having a very strong push. Enterprise, most of your big enterprises are still like an American Express, right? Or like, and in the largest companies,

A (31:39)

sorts of businesses that are most likely to adopt the cool new technology are the startups that are using ram, right?

B (31:46)

There are more. Those are the exact same companies that are more likely to be using the hot Silicon Valley company, which is anthropic. And whereas your general Fortune 500 they know OpenAI they like. And so that's probably a very unreliable I put a sentence when I quote that saying this might not be a reliable chart, and especially this. So I'm not. I've gotten lots of pushback from companies and I've gotten very good at ignoring it. But I do think actually this is one where I'm going to give OpenAI the benefit of the doubt.

A (32:14)

All right, and that is the end of the free preview. If you'd like to hear more from Ben and I, there are links to subscribe in the show Notes, or you can also go to SharpTech FM. Either option will get you access to a personalized feed that has all the shows we do every week, plus lots more great content from strytechery and the strikeri plus bundle. Check it out and if you've got feedback, please email us at. Email sharptech FM.