The thing that is fundamentally different about this product cycle is that the software itself can actually do the work and therefore the market opportunity for software today is no longer just IT spend. It's largely labor. It's not like all the jobs will go away. I actually think that's not going to happen at all. There are a lot of things where if I could hire somebody for a dollar to do this task, I would 100% do that. I've never been able to hire somebody for a dollar. Now I can hire software for a dollar. While it is important to understand model capabilities and what's happening in the frontier, you still need to figure out how to apply that technology.

I think modes matter just as much as they did before. The one change is that in this supply demand equation, there's conceptually more supply of software on the because the barrier to creating this stuff has gone down dramatically.

I think AI is an incredible tool for differentiation. The idea that a voice agent can speak in 50 languages fully compliantly 24/7 highly differentiated you know certainly versus the human theess of that capability in my opinion is not a source of defensibility it is just so consensus like cloud was not consensus mobile was not consensus and that's why the incumbents kind of screwed up. We've spent a lot of time talking about moes and how moes have evolved and are there still even moes in in this new era. And so why don't you reflect and share some of the conversations we've been having or some of your your perspectives on this broader broader moat question. Maybe David we'll we'll start with you. Maybe just to jump right into it with with a hot take. I think moes still matter and I think a lot of the moes um still matter. Still matter. Exactly. Um and I think they're largely the same. Right. I think you know I often think about this between uh sort of differentiation and defensibility. I think AI is an incredible tool for differentiation, right? The idea that, you know, a voice agent can speak in 50 languages fully compliantly 247, highly differentiated, you know, certainly versus the human. Um, but the source, the AIS of that capability, in my opinion, is not a source of defensibility. It it's largely differentiation. The defensibility of a software product resides in my opinion you know from owning the end workflow you know from the context in which that it's it's applied you know becoming the system of record having a network effect you know deeply embedding yourself within your customer and I think these were the heruristics that were always you know things that we would always look for when evaluating software companies I think the thing that is fundamentally different about this product cycle is that the software itself can actually do the work right and therefore the market opportunity for for software today is no longer or just IT spend. It's it's largely labor.

The challenge often has been that everybody can build something at small scale and a lot of the they kind of I wouldn't call them network effects but some of the defensibility modes only become uh apparent at large large scale. So like a lot of people talk about like okay take an example from like the the long long time ago pre prei era um if I am building an anti-fraud company and I've seen lots of people right am I going to do a better job than a net new anti-fraud company that's seen a few people and the reason why this would be called a data network effect although um there's another podcast that Martine and I did a long time ago debating whether or not data network effects are real but it's something that really it's almost like gravity gravity actually like one atom actually has exerts gravity on you but you only really see it at like very very large scale like the earth you notice the gravity the sun you you notice the gravity Jupiter you notice the gravity you don't notice it for like that glass and it's the same thing for a lot of these data network effects where at very very small scale when you have 20 companies that are all saying I'm going to stop fraud like all right they're all building the same things they all have the same algorithms but when you've seen four billion people and you know like these people are bad Now you can sell each incremental customer each customer of your anti-fraud technology to to use this example because you've seen more customers and you can get actually better results. But the challenge is that a lot of these these moes only really are evident at mega mega mega scale. And the same argument would apply. It's like oh like I've seen four customers. David's seen three. I've seen four. He's seen three. Pick pick my software. But it's like you've seen four customers. That means there are eight billion customers you haven't seen. there eight billion customers he hasn't seen like what's the difference whereas um at mega scale it's like all right I've seen four billion customers he's seen one billion customers well it's actually kind of easy to see that the results of my product will be better but that's at scale um and a lot of the question is like on the 0ero to one phase it's hard to make the argument that like I have better like if it's fraud I have better fraud underwriting if it's you know AI do the work like I've done more phone calls to a particular type of customer and therefore I do a job. It's hard to make that argument at subscale. So, and this is often the challenge is that it's kind of self-evident that if you become the biggest company in the world, then you have a moat. But how do you get to the scale where you actually could show that you can't get to that scale if you have 9 million ankle biters um and you are yourself an ankle biter of just we are trying to get to scale and nobody can because it's so easy to actually produce software. And that's kind of the that's the double-edged sword of AI is that it's very very easy to produce software. Um, everybody can go do something that is a very obvious idea because it's obvious everybody's going to go build it. But can you get to the type of scale where you actually could show a mode and that that has gotten, you know, arguably harder because you have a larger end count of potential c sorry uh potential competitors. Um, but if you get to mega scale, then you could show the moat and that that's kind of the zero to one versus one to end.

And it's the same thing for a lot of these data network effects where at very very small scale when you have 20 companies that are all saying I'm going to stop fraud like all right they're all building the same things they all have the same algorithms but when you've seen four billion people and you know like these people are bad Now you can sell each incremental customer each customer of your anti-fraud technology to to use this example because you've seen more customers and you can get actually better results. But the challenge is that a lot of these these moes only really are evident at mega mega mega scale. And the same argument would apply. It's like oh like I've seen four customers. David's seen three. I've seen four. He's seen three. Pick pick my software. But it's like you've seen four customers. That means there are eight billion customers you haven't seen. there eight billion customers he hasn't seen like what's the difference whereas um at mega scale it's like all right I've seen four billion customers he's seen one billion customers well it's actually kind of easy to see that the results of my product will be better but that's at scale um and a lot of the question is like on the 0ero to one phase it's hard to make the argument that like I have better like if it's fraud I have better fraud underwriting if it's you know AI do the work like I've done more phone calls to a particular type of customer and therefore I do a job. It's hard to make that argument at subscale. So, and this is often the challenge is that it's kind of self-evident that if you become the biggest company in the world, then you have a moat. But how do you get to the scale where you actually could show that you can't get to that scale if you have 9 million ankle biters um and you are yourself an ankle biter of just we are trying to get to scale and nobody can because it's so easy to actually produce software. And that's kind of the that's the double-edged sword of AI is that it's very very easy to produce software. Um, everybody can go do something that is a very obvious idea because it's obvious everybody's going to go build it. But can you get to the type of scale where you actually could show a mode and that that has gotten, you know, arguably harder because you have a larger end count of potential c sorry uh potential competitors. Um, but if you get to mega scale, then you could show the moat and that that's kind of the zero to one versus one to end.

And a lot of the question is like on the 0ero to one phase it's hard to make the argument that like I have better like if it's fraud I have better fraud underwriting if it's you know AI do the work like I've done more phone calls to a particular type of customer and therefore I do a job. It's hard to make that argument at subscale. So, and this is often the challenge is that it's kind of self-evident that if you become the biggest company in the world, then you have a moat. But how do you get to the scale where you actually could show that you can't get to that scale if you have 9 million ankle biters um and you are yourself an ankle biter of just we are trying to get to scale and nobody can because it's so easy to actually produce software. And that's kind of the that's the double-edged sword of AI is that it's very very easy to produce software. Um, everybody can go do something that is a very obvious idea because it's obvious everybody's going to go build it. But can you get to the type of scale where you actually could show a mode and that that has gotten, you know, arguably harder because you have a larger end count of potential c sorry uh potential competitors. Um, but if you get to mega scale, then you could show the moat and that that's kind of the zero to one versus one to end.

And maybe talk about what's different about defensibility for even the the bigger players today in the AI era than it was in let's say the web two era. Are the companies today more defensible, less defensible, or how should we think about sort of the strength?

I don't know. I I think the the less defensible part I mean this is this is why a lot of enterprise software has gotten beaten up in the public markets. It's kind of two reasons. is number one is that if you're doing per seat pricing like how do you come up with a pricing model that people feel is fair and a lot of it is just psychology and for whatever reason for the last 20 years it's like per seat per month with like uh you know you you've heard my joke the the tall grande venty model of like software uh charging it's like somehow that felt fair and whether that is fair or not like I don't know but like people are like oh yeah it's like $85 a seat you know per month yeah okay that sounds reasonable whereas if you if you propose that pricing 40 years ago, you would have been laughed out of town. So, this just became the norm. Um, and the reason why, as I was saying, public software companies have been beaten up a little bit is like, uhoh, maybe you won't sell as many seats. Like, is Adobe going to sell as many seats if now you don't have to hire as many graphics designers? Or is Zenesk going to sell as many seats if the software just answers all the queries? Like, the answer is no. It doesn't mean that the companies are toast. they might actually quadruple their revenue because now they charge per outcomes as opposed to charging perceipats. But that's kind of part one.

Part two is wait a minute now everybody can vibe code up a Zenesk competitor. So maybe companies will just start they'll stop buying software. This one I don't think we've seen at all. Um but I think there is that like these two-sided um these two risks. But to answer your question, does defensibility change? Well, if you now are able to code your own software, like why am I paying like your margin is my opportunity. Well, look at the margin of software companies. Like Salesforce has like an 80% gross margin. Like they should have a 1% gross margin or you know nobody should use Salesforce anymore. That that would be the procase of Moes really starting to disintegrate. But I don't think we've seen that happen at all. Um because it turns out people um on on the one hand two things are actually happening. One is that this is kind of like Clay Christensen theory. It's like the incumbents overshoot the market. So the amount of features in Salesforce or Zenesk or Netswuite, it way exceeds the feature set that you need that any individual customer needs because it's meant to encompass. It's like all of these weird edge cases and you kind of see this if you use Microsoft Word. When was the last time you wrote a book? When? Never. Right. I haven't written a book. It has all of these things. They probably have 50 software engineers. Yeah. make make but but if you do write a book, guess what? Microsoft Word has all these features just for book authors to like make a table of contents or something. It's like I don't use that. So they they keep bundling more stuff in there so they overshoot the market and theoretically it's going to make it easier for somebody but you know so that but but kind of going back to what I where I started with this topic. Um like it turns out that this concept of I'm just going to vibe code Microsoft right it's like there are all these there there are these edge cases that you just don't know about. So it it's actually, you know, why don't you grow your own food or weld your own aluminum or build your own house. It's just it's kind of easier to use this concept of comparative advantage um and just say I'm going to buy something off the shelf. So anyway, so I think modes matter just as much as they did before. The the one change is that in this supply demand equation, there's conceptually more supply of software on the come um because the the barrier to creating this stuff has gone down dramatically. I I think the flip side to that too is that um while while there will be more software and and again the kind of marginal cost of producing software is you know declining asmtoically towards zero um the way that these companies are getting more deeply entrenched within their customers has has differed because again the software is doing the work and therefore in many cases it's actually replacing labor. And so if you've transitioned a team out that has now become, you know, your software, like you're now much more dependent on that product to run your business, um, you know, and again, you know, is it more difficult to to replace that software with another piece of software or to rehire that team? I think it's an open question, but again, the software is is doing more of the work and therefore, I think, getting more deeply embedded within their customers.

Well, part part of it is just like the Goldilock zone of pricing. So, um, and I I wrote some tweet or whatever it's called X thread about this a long time ago. I call it the janitorial services problem because if I went to you, you're the CEO of a giant company where you write your books, um, in the future. So, you have a 300,000 person company. I find you, I was like, Eric, I can get your toilets 9% cleaner and save you 1% on your toiletry spend or your your janitorial services spend. Not only do you not care, you don't even care enough. you don't you won't even like exercise the mental energy to find the person in the company who does care, right? And that means that your janitorial services spend will never change. And the problem is it's hard to get in. The good news is it it's hard to get out. Um whereas for something it's like 90% of my profits go to like you the or to I'm now 90% of your profits as the CEO of GE. They're going to me. Your number one priority is like getting the hell off of me, right? And like doing RFPs left and right. So part of it is also just like how relevant this is. And there are some companies that operate in this Goldilock zone of irrelevance like these janitorial services where even if you have 9 million competitors like they're just not going to go anywhere. Which is why like a lot of the strategy that we talk about internally is green field, right? It's like those companies are they're they're stuck for good. Um, is there a a high rate of new company creation that will not use the crappy old janitorial services company but will actually resonate like your pitch of like I will get your toilets cleaner and I will charge you less money that really resonates but that's that's not going to resonate to the people that are using the oldfashioned stuff.

What are examples of of of company or space in the Goldilock zone and what was an example of companies or spaces in the green field zones? Well, like payroll companies, right? like um ADP and paychecks I mean these are companies that are collectively worth hundreds of billions of dollars um very very profitable and how does pay like you could do your own payroll actually it's kind of a good metaphor for software in general like why is it that you have to like why can't I just pay you you're my employee why can't I just like cut you a check well because I have to withhold taxes well how much tax do I have to withhold well it depends right and there is this like super complicated lookup table it's like well you live in this county but you spend this many days in New York and this that and the other thing oh and you you you you you owe like child support and the IRS is garnishing your wages, like all of these things that are very complicated. So, it turns out it's just cheaper to go to ADP and ADP just charges you like I don't know like 50 bucks a month per person that you might be paying 100. It's a it's a poultry sum compared to the overall amount of payroll. So, nobody really switches their payroll companies. Like that would be an example of one. On the other side, um I had a lot of companies in coming out of 2022 where the market really went through a downturn and they're like, "Wait a minute. I'm spending four I I had a thousand employees. Uh I downsized to 200 employees. I had a,000 licenses for Salesforce, right? What's a,000 time $100 a month times 12? That's $1.2 million a year. Wow. Like that's a lot of money because I only have 200 employees and I only have six months of cash. Like I got to save that." and they didn't do that for their payroll spent. So you see it um uh like a lot of companies do want to rationalize their overall software cost especially for these things where they recognize in aggregate like most people aren't actually using the seats. Um, so I'd say like, you know, Salesforce type stuff. Um, you know, some of the creative tools like if you like Adobe is very expensive and you might just do like a wall-to-wall license saying, why not? But then you look at if you're like, how do I save $5 million? Nobody's using this. Well, it's $5 million. Whereas for things where inextricably the delivery and the payment are linked, right, which is very very different than percemole. Like obviously I'm not going to pay for payroll services unless you were employed here. Whereas I might like we have 600 people that work at our firm. I think we have 600 licenses from Microsoft Office 365. Like we probably I bet there are a lot of people here who have not opened Microsoft Excel in a year. So why are we paying for that? And that would be the idea of kind of rationalizing software spend. Um so it it it kind of depends, but I think per seed pricing where it's like it's just easier to pay for the entire thing wallto-wall, you know, your in your entire organization, those are often the first to go versus things that are again inextricably linked to the actual usage.

Yeah. So you mentioned earlier that we've seen you know basically you mentioned uh there was this concern that maybe instead of zenesk it will you know companies will you know there'll be a vibe coded version of it but we've seen none of that. So, so far is your mental model is we'll we'll see it to the in examples where the the cost is significantly high or in which there's sort of green field opportunities or what is sort of your mental model for the types of software that will replace? Yeah, I mean I think the green field one is always true but when you look at green field opportunities you need two things to be true. You need the entrepreneur to be very very patient and say I'm not going to try to sell to everybody who's if I'm if I'm starting a net new payroll company I'm not going to try to sell to GE because I recognize that they are they are hostages to ADP and that's never going to change. So one is that patience of entrepreneur and the other one is you just need a a high enough rate of new company creation to really make it work which is why um like to pick on one space of electronic health records or electronic medical records how many new hospital systems are created every day I mean it rounds to zero so if I'm trying to go build a new EHR system to go compete with Epic or Cerner I can do that um there are a lot of edge cases there but it's like and I might have patience as an entrepreneur but wait a minute like I need to sell $5 million deals to big hospital systems. Every single hospital on Earth is currently using an EHR system. Going to be really really hard to make that work. So I think I think both of those need to be true. Like the right type of entrepreneur who's willing to be patient because it's it's often a very lonely game of it's like I built this great product. Wait a minute. I don't have any customers yet. And you want to see high traction because you're seeing in the rest of the market like some companies are just going like this and my company's not and I'm in Silicon Valley and I need to recruit the best people. It's like they want to work at the company that has the graph like this, but you need this green field requires patience.

Yeah. The So, we're talking about how Moes still matter and in in many ways they look pretty similar. Let's steal man the other side for a second. Why are we even having this conversation where some people say, "Hey, you know, brand is the is is is shipping velocity or because this era is different." What are the what what's the steel man of of their argument?

Well, look, I I think this market is noisier than ever, right? And so I think finding ways to sort of you know stand out from the crowd probably matters more today than it has you know in the past I would argue. I think the other thing is that the the underlying technology is changing so quickly. And so you know as a founder you want to be living on the frontier and understanding kind of what model capabilities look like because it can dramatically change the the efficacy or the the um you know the capability of your underlying product. Um and so I think you know um you know one of the things that's changed I think that's been really interesting in this sort of um you know current wave of especially vertical applications that we've seen is is the type of founder. You know I think founders today are often younger and more technical than we've seen in in prior generations. um you know and and so they're less often native to the particular industry but they're fluent in the tool set right and I think that's really important because you know to the same point you you got to you got to stay on the frontier and understand what's coming at the same time you know I wrote this piece that I call context is king you know while it is important to understand you know model capabilities and and what's happening in the frontier you still need to figure out how to apply that technology and so while the founders themselves are maybe less native to the particular industry they're still hiring for context, you know, very early in a company's life cycle. A good example of this that I I sit on the board of is a company called Eve. You know, the two founders of Eve were the earliest employees at Rubric, which is, you know, now a public infrastructure company. Um, you know, they built a legal AI company in the plaintiff law space. Neither of them had any particular background in in employment law or or personal injury, but they deeply understood, you know, how to apply, you know, document extraction technology and and sort of, you know, voice and LLMs more broadly to this very particular work, you know, uh, workflow. And they've hired plaintiff attorneys actually on staff. So anytime a new model is released, you know, they're understanding, you know, from people in industry the impact that it's having on on drafting on, you know, their ability to, you know, to reason through a case, you know, or a matter. Um and so again it's sort of this tension of like you know building the brand having momentum you know understanding what's happening on the frontier and yet you know figuring out ways to apply that technology in the context you know of your specific customer because again I I I deeply believe that that is where a lot of the sources of defensibility reside. You know I'm I'd love to find other examples of businesses is um where the technology like reinforces their business model. It doesn't compete with it. Meaning in lots of areas of legal um if you make your employee 50 times more efficient you're eroding your billable hour. In their business they operate at a contingency basis meaning you know they only get paid if they make if they win. So there's no sort of limit to the amount of AI that they want to adopt. Uh and if you can become 5x more efficient you can take on 5x more clients. Um anyway these are sort of characteristics that I think you know I'd love to find more of and hopefully that can be kind of a bad signal too. I think the other steelman is if you believe that brand matters which it almost taologically does because what do I buy? I buy the thing that I've heard of, right? So there's an advantage there. And if you believe that for a lot of companies and products, somehow having scale is effective, right? So not a network effect, but a scale effect. So if I'm Honey Nut Cheerios and I know that people are going to buy lots of my Cheerios, I can I can build a big factory and not, you know, hand crank out each Cheerio. I'm going to have these compounding advantages just in terms of economies of scale, right? Like Amazon is that does that really have a network effect? No. It's like it's kind of nice that everything that I buy will show up the next day or in two days and how can they do that at low cost because so many people are buying things. So there are some things that have scale and those things also benefit from brand. So if you can move the fastest, right? So if you can elomerate capital and labor so it's like I raise the most money. It's a very very generic idea, but somehow like most other things on planet Earth, if it's the biggest and like really really big kind of gravitational scale, then it's just going to work better. So, can I get there the most quickly? But there are 20 companies that are doing the exact same thing. And at that point, I wouldn't say that momentum is a moat per se, but momentum has the highest chance of getting you to gravitational scale where you do have a moat. And if you don't do that, by contrast, you're just going to get eaten alive because you can't hand crank out the Cheerios. You you have to get to the scale where you're able to build a factory. And with the you have the biggest factory, you can crank out the most things at the lowest cost. So, what is the trajectory? What is the slope of you versus all of your competition? And if you have not a good slope, um you're you're just not going to win that game.

One of the questions for defensibility in in web two companies was hey would Google you know would those will they some someday build this or or Facebook or name your incumbent um in in the AI era it's will open AI or will will some other you know major company how should compan how should we think about that that framework in the AI era you know I mean it's funny I feel like 18 months ago this uh you know GP GPT rapper was on everybody's lips and I think it was it was largely used as a projorative you know, it's like and I think, you know, to some degree, I think there are some spaces where like the model capability and the application capability, if they're very overlapping, I think you're in a in a risky spot, you know. Um, but the reality is that there's so many I think one of the remarkable things that's happened is there's so many markets that were never particularly interesting to sell software into that are now radically interesting spaces to build companies in. Again, in large part because, you know, the market is now labor, not just IT spend. plaintiff law being an example, you know, uh, you know, Alex has we have a company called Salient in uh, applying voice agents to autoloan servicing. Five, six years ago, would we be backed a software company selling to, you know, non-bank auto lenders? Probably not. The company's doing incredibly well. Again, in large part because, you know, the capability of being able to, you know, uh speak in 50 languages, you know, fully compliantly, you know, with with customers in 50 states working 247, um, you know, is just so differentiated, you know, uh, versus the individual. And they're finding that their ability to collect is meaningfully higher, you know, than than that labor that the that the kind of costbenefit trade-off is so dramatic. the company is getting a lot of you know revenue from those customers who may not have had um you know millions of dollars of of IT budget historically and are now very willing to pay for a product like that you know given the impact on the business

and and the way that we used to talk about this a long time ago is uh and this almost had a porative slant to it but it's like are you building a feature a product or a company and what's the difference between the three well a feature is like there's an existing product and you tweak that product to make it marginally better a product is, you know, not that. It's like some hopefully system of record or something that keeps track of something and then uh a company is probably the most defensible of those three where you have a product and you know maybe you own a platform like the platforms tend to be the most valuable companies but you know a feature is like I've built a Chrome plugin and that doesn't mean and there by the way there were a lot of Chrome plugins like Honey was a Chrome plugin that got bought by four for for$4 billion like I wish I had done that right that's that's a good feature but that was a feature you know, a product would be like, "Oh, I built my own browser." And a company is like, "All right, well, like my own browser company actually makes money." Like, you don't actually have a company, even if you have 10 products, if you don't have a sustainable path to have that company be around in 10 or 20 years. Um, and I think kind of another way of thinking about what David just said is that now the features, like, you know, the feature was the most porative and seemingly small of all of those three almost obviously. some of the features can be incredibly profitable because it's like wait a minute like this it feels like a feature um because it could get added to Salesforce right or could get added to one of these other things but the amount of money that I can charge for my feature is like orders of magnitude more because it's like hey I'm going to be the front office receptionist for your you know orthodontic clinic like that's my job like that's my that's that's that's the feature and it sits on top of whatever software you currently use, but the feature I can now charge $20,000 a year for because it is doing the job of labor. But uh-oh, will the existing product that my feature is riding on top of, will they go build those those pieces of functionality andor will another company show up that just says, "Hey, we're going to sell the green field with a new product that kind of has this feature set embedded." and you know feature product company it still is out there but um I've just never seen a world where the features if you will can can get to revenue scale as quickly and by the way you you kind of often have to start with the feature because a customer isn't like think of it from the customer's perspective the customer being the business buyer of software it's like I know I want to be locked into a piece of uh software company for 20 years that's what I'm looking for as a buyer no it's like oo I have a problem to solve my problem is I can't hire a front office receptionist for my orthodontic clinic or I can't call people in Mandarin or Cantonese to go like repay their auto loans. Like what do I do? Oh, something shows up and it offers that functionality. Boom, I'm a buyer. And then that functionality has to that that feature has to backfill product, backfill company as quickly as possible. So that's still true today as it was 10 or 20 or 30 years ago. Um but the difference again is that the feature the the revenue for the feature is just so high and the demand for it is so high because again in many cases you're just responding to help wanted ads effectively.

Yeah. And so I think the effect of that is that there's been sort of like a Cambrian explosion of interesting markets to go after you know I think it's unrealistic to believe that like OpenAI is going to go build you know the the the you know front office assistant for the you know the dental clinic like as their core you know kind of business. They're not going to do that across every single market. I think the other dynamic is that for many of these companies, part of the product value is actually orchestrating the work across lots of different model companies. And so I think having one, you know, uh, you know, foundation model business, you know, going kind of up the stack, I think limits the actual impact of the actual of the application, you know, potentially as well.

Well, I think that, you know, if you kind of think about this versus other platform companies. Um, so Facebook was the pre-eminent platform company of of web 2.0. So call it from when whenever they opened up Facebook platform which I think it was like 2007. Um people built their businesses on top of Facebook. Facebook would never do those particular things. Like so Facebook is never going to show up and say hey you know what we should build a farming game. Like they were like no we're going to have a platform that allows companies like Zingga to build these farming games. But what the platform normally does if they don't actually go compete with the the underlying products is they say, "I'm going to tax it, but I'm going to tax it in ways that are kind of at my fancy. So this week it's 10% taxes. That's my promise. Oh, wait. I changed my mind. Now it's going to be 40% taxes." So that's why it's always dangerous to build on somebody else's platform. So I think the two things to look at are number one is will the platform owner compete with what I'm doing? Um, and that's also another Goldilock zone question, right? Because why is it I I published this graph of VisiCalc versus Lotus 123 versus Excel. So VisiCalc invented the spreadsheet in 1979 had 100% of the market because they were the only player in town. Lotus built a better version of that. Uh Lotus got to like I think 70% market share by 1985 which was when Microsoft released Excel for uh a Mac. Um and then by 2000 uh Microsoft had 96% market share. And why is it because they owned Windows? Like the the platform owner normally wins. So, but that's because it was just such a hu like why do I buy a computer in 1997 because I want to use a spreadsheet like it was just so intrinsically linked. Like that was one of the main use cases for computers and business use, right? It's like using spreadsheets. So that was like violator of Goldilock zone. Whereas other things where it's like all you have to worry about from the platform owner is that they're going to tax you, but they might tax you in very very bizarre ways. But uh part of what David was saying in terms of like there are multiple model companies, which is great. Like the problem with Windows was that it was like 95% of the market. Like 95% of your customers used Windows. So if I'm going to go build a competing spreadsheet, I'm just toast because the platform owner is just going to drown me. Um now there are five model companies or you know more like when you include all the Chinese models and whatnot open source like I don't have to worry about that. But I do have to worry about them saying, "Wow, this is so relevant." Like, why is it that OpenAI got a public company CEO to quit her job and just to become the CEO of of applications at OpenAI? Maybe because they have a huge application opportunity. But this is the nice thing is that a lot of these things are so obscure, but they're still big. But I don't think OpenAI is going to go do them because it's like, are they going to do like dental care management? like they they could, but if they've done that, then I would be short Open AI because it's like they've run out of good stuff to do.

Um, that's something that they should do in 2029. And then this is I think I told you this this story before. This is I I this changed my outlook on life when I pitched this guy Dan Rose at Facebook who was running business development there. I'm like, "This is a huge opportunity. You should use us for payments. We're going to do this. We can make so much money for Facebook." And he was so patient and nice and I I love this guy. I'm on a board with him to this day. He was like, "Alex, that's such a great idea." I was like, "All right, I got the deal." Yes. He said, "It's a great idea, but we're not going to do it because you're pitching me a go. Like, we have gold bricks all around us." Like, and he was right. I mean, like Facebook in 2010, I mean, how much money Facebook has grown their revenue pro? They they have more profit every quarter today than they had revenue per year in 2010. It's just such an incredible company. And he's like, "You're pitching me a gold brick that's like 100 feet away." And it's real. like I love that gold brick, but we have like hundreds of gold bricks where I just have to like stoop down at my feet and pick them up. So, I'm just not going to do that one right there. And that's how these big companies think. Um but the nice thing is that these are gold brick. These gold bricks are bigger than they've ever been because you have software that can do the job of labor.

Yeah. Um, which on that note, if if you were uh running OpenAI and you were thinking about wh which gold bricks or how do you even what mental model to think about sort of what what are the things that you should be doing first versus things that hey maybe let let other people do it. How would you be thinking about that question?

I mean I think a lot of it is where well it's it's two things. Number one is we want to be the backend for everybody like the platform. I think it's two things. Number one is can we be the platform for pretty much everybody who's building anything. So, we're not going to go in these into these obscure spaces like, you know, orthodontic care, uh, at least not until, you know, 2045. So, let's make sure that every single developer is using us. Um, and this is part of why Microsoft crushed Apple in the 1980s because Apple made it really hard to develop software. Um, and what's actually kind of interesting is that both Apple and Microsoft um, had like Microsoft started off as a compiler company. Like their very very first products, they were not Microsoft Office, it was not DOSs. They built a basic interpreter um for the programming language basic and they had a big business. Their their biggest competitor was Borland um which only made compilers and like the early rallying cry if you talk to any early Microsoft employee was beat Philippe. Philippe Khan was the CEO of Borland. So Microsoft was focused on that made a lot of money on that and Apple was like we should make money on that too and they had a product it was called MPW uh Macintosh programmers workshop. I remember I used to use it in the 1980s and uh it was like $2,000 I think in 1980s money to buy this you know IDE or you know programming uh thing and uh it's like how do you afford that? So like but it was like we have to make money on that. Microsoft's making money on this and then lo and behold there were like 10,000 times more you know DOSs and Windows software products than there were Macintosh software products. And of course, Apple corrected that mistake when the iPhone came out when they now like Xcode, which is the way that you build products for um for Mac products or Macintosh and and iPhone iOS, it's free. So like they they kind of corrected that mistake. Um but I'd say two things to answer your question. Number one is can we be the biggest consumer brand in the world? So Chat GBT has 800 million weekly active users. Like get that to five billion, right? Like is even if Gem Gemini 3 came out today, it might be five times better. But are people that are using chat GPT just as consumers, are they going to switch? Like maybe, but it's unlikely just because they kind of make that their their default and then be the backend for everybody who's building anything. And that way it's like kind of all the gold bricks kind of come to you. I think the other uh thing that we should anticipate, we're already beginning to see from some of these big model companies are like what are the big horizontal applications that they can likely sell to every you know large enterprise. And I think you know you saw today with you know Google's uh anti-gravity launch like the ID is going to be one of those things. I think like that you know if there's like product market fit for for LMS like you know coding is definitely you know one of the top categories. Um so I think that you know thinking about what are the big horizontal kind of applications in the enterprise. I think there's also to some degree and you know we'll I think this has been earlier to sort of play out. It's sort of the palunteer opportunity. I think we're still very early in in sort of the proliferation of this technology into large enterprise. Um at the same time you know unlike prior product cycles you know you know like the cloud if I'm the CEO of a large public company and I'm asking myself do I need to be in the cloud? It was sort of an esoteric idea. you know, today I can plug a, you know, prompt into any one of these models and intuitively understand the impact that it could have on my business, right? The the efficiency gains in my customer support organization, in my engineering organization, in all of my back office functions. At the same time, many of them don't know where to start. And so I think you will see sort of this consultative sort of forward deployed palunteer-esque sort of sale into very large enterprise from some of these, you know, big model companies. Again, I think we're early in that, but you've you've heard inklings of this with um you know, with Enthropic talking about wanting to build into financial services and and other markets. So, you know, I agree. I think the biggest opportunities are the one that Alex is describing, but I think you will see them selectively, you know, try to build kind of applications that cut cut across every one of those and then they'll probably choose, you know, a few sort of like lighthouse customers to build, you know, largely bespoke kind of custom integrations into these, you know, bigger enterprises. But where are the ACBs, you know, just really make sense.

In in in web two, there was a lot of winner takemost. Um, you were talking about one of the benefits in AI is that there's multiple winners. To to what extent is is consolidation in in inevitable or how do you think sort of this this plays out? Well, I think if you have 20 companies that are all doing the same thing, um, what has historically happened is that it's a bad market if there are 20 companies doing it, but then I don't know, the bottom 15 just go bankrupt. Um, and then maybe there's some consolidation where number one buys number two, number two buys number three, and assuming that we have a functional FTC and whatnot, it's like all of this is approved because it's not like you're taking this is like orthodontic clinic answering software or something. Um so and then what was a bad market becomes a good market. Um and this kind of goes back to like why momentum is important because if you have 20 companies that are all at the exact same scale um then it's actually great for the customer which is like the the prices go to zero um or they converge on the price of electricity. Whereas if you this is not saying you want to go build a monopoly in orthodontic answering software or something but rather you can charge more if you get to a certain scale because whatever the the quality of the product that you're delivering at the end of the day is just higher um and you have to get to the critical scale to get there and sometimes you just need these markets to to work themselves out. I mean like when I was running my company trial pay we had I don't know 20 competitors and it was tough because it's like you know um everybody would be pricing their product at a loss you know this this loss leader only works if you end up leading with like you have to make money at the end and nobody really had a plan for that because the venture capital dollars were really subsidizing everything and that does not get a good market. what does become a good market at the end and sometimes this is what you know Vista the private equity firm would do is like we're going to buy one as our anchor we're going to go lowball um and put the other five out of their misery and now we end up with actually a pretty good product at the end or a pretty good business at the end pretty good company at the end so I think that will probably play out the same way here because you just can't have a market where you have everybody lossle leading um and nobody's big enough to get any kind of scale effects um is there going to be a world where the the 19th player survives. I mean, Jack Welch uh would always say you have to be number one or number two and there's no value to being number three through 100. I don't think that's changed.

Right. Right. Even in the model provider example and I'm also curious if prices go down. Yeah. I I don't I don't see how like there actually are I mean people know XAI, Anthropic, OpenAI, Gemini, like they they know or Quen um they they know the big ones but there are actually there's a long tale of things that people haven't heard of um where it's like they've raised lots of money. It's just like not it it's it works fine, but how can you surv like the model company is the most cutthroat because like unless you're state if you're state-of-the-art minus minus minus and you're trying to earn a living, it's just like that that's just not going to work. So that game is super cutthroat. I think I think the one area where that um may have diverged and Martine talks about this a lot is like um you know when markets are growing so quickly you you end up having specialization and so I think in other kind of modalities you know in in some of the creative tools or you know people have specialized to like serve you know the up market you know like I'm I'm producing you know movies okay I want to create sort of like social you know quality content like these are different you know markets that that the models can kind of specialize in time will tell you know how sort of uh you defensible those become over time. But um maybe that's the optimistic take that like you know early on everything looks you know overlapping and competitive but we're still so you know the market is growing that everything can kind of expand and people can kind of specialize over time.

Earlier when you were talking about the feature versus product didn't Steve Jobs once tell Drew Hston that Dropbox was was just a feature. Yeah. I mean that that's why it's always been this porative thing but that's that's kind of the point that I was getting to is that nobody wants to like oh I need this company. No, it's like I need this feature. Um, every now and then you see a product that is not a feature because it's just like so far out of left field. Like nobody was anticipating chat GPT dominating their daily workflow in 2022 in October. Um, but then once it came out, it was this like, holy crap, I this is incredible. And that's not a feature. You could argue it's a feature on top of your iPhone, but no, the iPhone is the delivery mechanism. That's a that's a product. Um, and they they've obviously turned that into a company. Whereas other things it kind of is like, you know, why is there anti virus software? That almost doesn't make any sense. Like, shouldn't the operating system stop you from getting viruses? Like, why do you need a third party tool to do synchronization between devices? But it turns out like the reason why Dropbox has survived and thrived since Steve Jobs made that comment is like it's really hard to do well. Um, and there's a lot of other things like once you've built that feature, you can backfill with all sorts of other product, which is what Dropbox has done a pretty good job of, but it is hard because this is the the danger of building on somebody else's platform is that, you know, I'm going to build this thing that they should have had, right, if they had the foresight. Um, and if it doesn't operate in the Goldilock zone, right, it's like, wow, this is so this will like triple Apple's profits. Let's just say that Dropbox would have tripled Apple's profits. Would they have dropped every would they have focused on building that versus the iPad or something, whatever like Steve's last gizmo was, like sure. But if it's kind of in this like Goldilock zone of irrelevance like janitorial services, it's like yeah, they should do that. But, you know, platform owners get lazy. Um, this is why like, you know, half the things on my iPhone don't really work if they're built by Apple. um try like any any parent that's listening to this if they've tried screen time it's just like an embarrassment upon humanity and because they don't have to go sell as a it's like they don't have to compete on feature they compete on the fact they don't even compete they just like they're the platform they roll it out it's going to be bad and that does create an opportunity for somebody to come up with the feature and actually out compete the um the platform but like you have to be careful because it's like obviously the platform owner is going to go compete with you and that's why often what I find very compelling about entrepreneurs when they know this like they've studied how is it that from every single platform shift from like you know we were talking about AC versus DC current like there have always been these battles for like who's going to be the underlying you know layer um the best entrepreneurs have studied this and they have a plan they're like I know I have a feature like Drew knew this he's like I know that like there's this stupid comment on Hacker News it's like oh this is just like our sync with this that and the other thing it's like yeah of course Drew knows that but he built this into a $10 billion company because like he had a plan and the best entrepreneurs they often like okay I know it's not this naive it's like oh I'm going to build this there's no way that they're going to build it because they're too dumb and stupid it's like no they're not like these companies if they get their act together they will marshall a lot of resources to go compete with you it might take them 5 years but they will 100% do it you have to backfill your feature with a product and you have to have a moat for that product as opposed to like oh yeah like the big company will never figure this out it's like that's not True. I I think what's also unique, I I wrote this piece a while ago called the messy inbox problem and [snorts] it was sort of a wedge strategy that we've been observing across lots of different industries and it's just this idea that um you hook into a bunch of your different unstructured data sources. Could be email, could be fax, could be phone. Um you know, Tenor as an example has trained a model to be able to extract all the relevant patient information from those data sources to plug it downstream into some system of record. in their case in EHR, but this exists in a CRM, an ERP, um, what have you. And I think that that wedge for that feature is interesting in large part because it lives upfunnel from software, right? You're replacing the kind of human level judgment of the individual. Like often that ad, you know, the secretary sort of like collecting the physical facts and then plugging it into the HR. And so now a bunch of AI companies can kind of, you know, wedge in and then eat away at all the downstream workflows that might have been their point solution software companies. And so, you know, Tener is no longer just doing, you know, the messy inbox. They're now doing scheduling and prior, you know, uh, prior and eligibility benefits. Um, and they've used that wedge to try to become, you know, kind of the endtoend platform. Eventually, maybe they become the system of record. Um but again because you can kind of replace the human labor now with software um I think it's creating opportunities for these you know features to actually become products and you know in in their case I think become you know whole companies. Well, I think I think this is the thing that in my mind is very dramatically different than every other platform shift is that the the it is just so consensus like cloud was not consensus, mobile was not consensus and that's why the incumbents kind of screwed up where it's like and then sometimes it was just like completely um I'll use the the Silicon Valley term orthogonal to their to their business model because it's like I sell $5 million a year products and wait a minute I'm going to charge $100,000 a month. like that's just hard like how do I pay my sales people? How do I make my quarterly numbers? So that's why like you know workday beat peopleoft um or that's why you know Salesforce beat Sevil. Um so all of these things played out but behind it was this concept of it's like that new thing that iPhone is stupid. Um like there's no version of the the famous Steve Ballmer clip of like him saying this nobody's going to buy an $800 phone with no keyboard. Um there's no version of that for AI. It's like how do you find a big CEO or even a small CEO is like nobody will use that tool that makes you a hundred times more productive of of course and this is why it's it's kind of a bonanza for most of the incumbents as well because anybody who has a system of record will add a button or a feature to use our parlance that will make them more money. Um so like they're just kind of gold bricks everywhere. And the challenge though is that there isn't this this kind of white space to occupy in the same way that there was for cloud or for mobile or for a lot of the web 2.0 things where it's like you just like the incumbents screwed up. They weren't paying attention. They scoff at this new technology. Like nobody's scoffing at this new technology. Like everybody's just trying to embrace it. But, you know, the opportunity often exists where a lot of the areas that just seem too small that don't have an incumbent at all. Like those actually might turn out to be like, you know, trillions of dollars of value. And that's kind of what makes it much more exciting than like last gen where it's like, oh, I'm just going to copy everything that was on prem and make it, you know, recurring billing cloud and I'm going to do that at a time when like the big guys say that's stupid and I don't get it. So some argue that you know mobile was was ultimately sustaining and that although there were you know net new companies and use cases that were you know hundred billion dollars like Uber and Airbnb etc that uh you know the incumbents you know some of them became trillion dollar companies you know guided by mobile when we look at the you know business impact of of the AI era um what's your mental model for thinking about sort of the incumbent startup or kind of net new company in terms of you know value capture

I I I think a lot of it is the same like unless you really screw up the the pricing model or like you know you're all per seat pricing it's very very hard to just get the market to adopt something that is just violently different and you're operating in the public eye and your technology team is bad there there are a lot of ants that need to happen I have a hard time believing that incumbents will really suffer um I mean there probably are some things like you know take take like one example of and this kind of goes back to distribution versus technology like all of these business process outsourcing companies these BPOS they're the largest employers on the planet. So like Tata, Whipro, Infosys. So if I'm JP Morgan and I say I need a call center and this call center needs to have access to like customer records and it needs to be safe and everybody needs to be trained like and I need to have like a 100,000 people that can answer the phone. You know who can do that for you? Infosys, right? Or Tata. Um Tata has already done the integration with JP Morgan in this case. they might just add AI and now they don't need a h 100red thousand people and they maintain that JP Morgan contract and they operate in the the area of the Goldilock zone where it's like they're going to make like a hundred times more money. That that's one case. That's the bull case for Tata. The bare case is like JP Morgan's like wait a minute like we should partner with the startup to do this or we should do this ourselves and now like Tata loses that relationship altogether. And it could go either direction. Like you I think a lot of these things are really up for grabs. But I I think the the default is that the incumbents probably will do well. But you can pick a lot of these cases. I mean this is why you see the public markets kind of don't know what to do where there is a case that is very very bad for a lot of software companies. But there's an alternative case which is like if you operate in the right goldilock zone um and you're you know you have the right momentum to actually build these things and embrace these new technologies like you'll maintain all of your customer relationships um and you're just going to have a more profitable business and it's not that you're going to do this like the the most compelling thing I think about AI that almost everybody gets wrong is like oh it's going to destroy all the jobs like our our beloved representative uh from Silicon Valley is like trying to like eliminate AI that's just so crazy that our elected representative wants to turn us back to farmers of of tangerines and whatnot in in Silicon Valley, but um which which again I think is crazy, but [snorts] uh it's not like all the jobs will go away. I actually think that's not going to happen at all. What's going to happen is there are a lot of things where it's like if I could hire somebody for a dollar to do this task, I would 100% do that. I cannot hire somebody for a dollar. I've never been able to hire somebody for a dollar. Now I can hire software for a dollar. So a lot of these tasks like you know look at how many people took taxis post Uber right and it's like did you hear people say like you probably took an Uber to get here today right would you have taken a taxi 20 years ago like no way right because it's like where would you find the taxi how would you arrange the tax it's just like way too complicated whereas once you make it very very abundant and less expensive like everybody's going to use this and I think that's the that's what Ro Kana and and his ilk are missing which is it's not like oh I'm going to go and say, "I'm going to like eliminate all the jobs." Like, think of it in that JP Morgan example that I just mentioned. It's like, wouldn't it be cool if every single customer of JP Morgan Chase could have their own personal friend that they could talk to every single day there that would help them with every single element of their financial life? Or it's like, I'm stuck downloading the app. I can't figure out how to get it set up. Oh, talk to somebody in real time that will help you about that. Why don't they do that? It's just like the cost is known. It's high. And then the value is probably low. And as soon as you can bring the cost down to zero, now you're going to start hiring AI in all of these different areas that you just would never bother hiring a human for because it's just like you can't train the human, you can't find the human, and the human's too expensive.

I think it's a good place to wrap, guys. Thanks for coming to the podcast. Most don't matter. Yeah. [music] >> [music]

I think it's a good place to wrap, guys. Thanks for coming to the podcast. Most don't matter. Yeah. [music] >> [music]