How is AI Different Than Other Technology Waves? (With Bret Taylor and Clay Bavor)

25:07And so, if you look at things like GDP numbers or productivity numbers, it obscures the individual impact that is not always fun or easy or good. And then similarly, it could be great in 10 years, but really hard over the next two years, you know? And I think probably all of those things are true. It's still like in valleys, like right in the middle of it. Yeah, it's just in the middle of it. My view is it is as transformational as the former of what you said that. Like I think it is truly transformational. I also think the transition will be awkward and probably slower than people think. In the last comment, because there was a long, too long an answer, but I don't think all parts of the economy can absorb intelligence equally.

25:47So let's just say we developed fairly generalized super intelligence. I will use the analogy. Like you can invent a lot of drugs, but if clinical trials still take a long time, you're not necessarily going to get new therapies rapidly. You have regulation, you have cultural resistance, you have all these other things. The technology is transformational. I think probably it will impact society on a more measured pace than I think a lot of the folks in the AGI community think, just because of the natural rate limiters of society around. It's not like intelligence is the only input to productivity growth, but I really do think it's transformational.

26:22And I think it's both in a great way and a kind of uncomfortable way. I think it's transformational. Specifically for you guys, I'm so curious. What is the state of human capital at Sierra? You're one of the leading AI companies, you could probably recruit anybody you want, I mean, given who you guys are, but at the same time, you're just saying, Brett, I can probably do a lot of what people at Silicon Valley companies used to do. Like what are you guys doing? You're living it day to day. Well, first of all, everything we do as a company is going to be the, you know, direct or at least indirect result of talented, smart, amazing people who are motivated to accomplish an important mission doing great work.

26:59And so, since starting the company in March of 23, we've grown immensely where the leaders in the space and the demand for what we've built has been overwhelming and we are just growing the company in terms of people and geographies and industries as quickly as we possibly can. And on top of that, though, it's like we look for every point of leverage we can find with AI. And so we aggressively use cursor in our engineering teams. And I don't know what the percentage today of lines of code written by cursor is, but it's pretty darn high and getting higher by the day. To Brett's previous point, it is today this immense force multiplier for talented people who, one of our most senior engineers, actually the first person to join the company after Brett and me, basically sent a coding agent over the weekend to just come up with a dozen PRs for things that he wanted to fix.

27:56And it came back and half of them were great. A quarter of them were total garbage and a quarter needed some work. But like it was in a way him working the entire weekend with guidance from him. Let me double click on the cursor thing though for a second though because Clay and I talk a lot about this. So we always like to say the way we think about it at AI first company is we're building a machine to produce happy customers. So that's why we think about it. And I think that's important because it's like, if something comes off the assembly line of machine that's like malformed, you don't just fix that thing.

28:27You say what part of the machine broke to produce the malformed item. And so just as it relates to, for example, software engineering, we have this philosophy like when cursor, which is the most popular copilot for software engineers to like write code, and now having some sort of more agentic flavors of it, if it produces incorrect code, our philosophy is, don't fix the code. fix the context that cursor had that produced the bad code. I think that's a big difference when you're trying to make a company driven by AI and just use AI because essentially if you just fix the code you're not adding leverage.

29:03If you go back and say what context did this coding AI not have that had it had it it would have produced the correct code. I don't want to pretend we're perfect here but that's the way we think about it. I really like thinking of our business as a machine. This is a clay ism. He said this once, like, right when we're starting the company, he said, we're building a machine to produce happy customers and like nerds night me. And I'm like, oh, we are 100 % building a machine. Employees roll their eyes. But it's like, fix the machine, fix the machine, don't just fix like the output of the machine.

29:33And I think with AI, it actually creates a very actionable framework for how to bring AI into the company. But somewhat ironically, like as Clay said, where we are hiring a lot of people, notably to me, all the AGI labs are hired a lot of people. So there's some, I don't know, the elephant in the room is it's not quite done. You guys are in the center of this. I mean, Brett, you're the board chair at OpenAI. Like, what do you guys do as Sierra? You're incredibly well -funded, you know, et cetera, et cetera. But like, you can't compete with a $300 million cop package for someone. Like, what is going on with hiring?

30:04I assume you don't need those people. Yeah, so we're an applied AI company. Let me just give you my view of the market. This is something you all could debate. So I think there's basically kind of three big categories of AI software companies. First are the foundation and frontier model companies. These are the folks that are somewhat infamously or famously competing for these scarce resource of these great researchers. Many of them like OpenAI are mission driven and trying to create artificial general intelligence. Some of them are more commercially minded and essentially building these models, which they then license or lease out to companies like ours.

30:38Then there's a category of people make tools on top of it. So the proverbial pickaxes and the gold rush, you need data labeling services and you need a data warehouse on which you can, you know, are you need a retrieval augmented generation, like a vector database to support retrieval augmented generation and all these things. Like, just to all say all these tools that one uses when you're building an AI platform. And then there's companies like Sierra where we make AI agents for customer service, customer experience, Harvey, who makes AI agents for the legal profession, I think companies like writer do it for marketing.

31:13So there's just all these sort of, say, vertical AI applications. And we're downstream of a lot of that. If we're doing our job, we're taking the best of these models and composing them to make these amazing experiences. Just like if you were VCs and someone, a software's a service company came up to you and said, step one is we're going to build our own data centers. You look at them very skeptically. You'd be like, really? Like why not just rent a server for a Amazon web services or Azure? I think the same is true of applied AI companies. It may not have been true a year and a half ago, a year ago, I think everyone wanted to like pretend they were cool.

31:50Oh, shoot. I mean, we had Clem from Hugging Face on and he was like, I deeply believe that every applied AI company needs their own foundational model and needs to build it themselves. I could not disagree more strongly with a single sentence. That was the trend in 2023. You weren't cool if you didn't have your own foundation model and adapt and character and inflection. It was like, those all ended differently. It turns out that unless you're a pharmaceutical company and you get pharmaceutical companies get protection from patents for an asset that has value for a long period of time, I've heard from multiple investors that foundation models are the fastest deteriorating asset of all time.

32:29So if step one of your businesses to burn through tens of millions or hundreds of millions dollars of capital before you find product market fit. And that asset has value for like a week. I'm not sure it's like a great business model. And so yeah, I could not... What's it? Very, very expensive carton of milk. Let's use the hypothetical example of training a frontier class model today. What do you think the usable life of that is? Like you got to amortize a lot of token generation in a pretty short period of time to make that worth it. It's complicated. There's different ways of looking at it. So I'll start with like, there isn't one, it depends on what type of model you're building.

33:06Deepseek somewhat famously in their paper talked about reducing the costs of building these models. But I think there's a difference between foundation models and frontier models. Frontier are really the best of the best. And this is what labs like OpenAI aspire to always have. And when you have the best model, you attract different customer base. It's sort of always been Apple's strategy with their devices and things like that. you know, there's a difference. If you think of these things as commodity, you'll take a different strategy. You say our goal is to have the most intelligent model. There are downstream benefits of that, you know, beyond the cost of those individual models.

33:41I actually think there's not a one -size -fits -model for most tasks now. There's a really interesting trend in model building called distillation, where you can take a very high parameter account model and essentially make a smaller parameter account model that's called 80 % as good and I'm just making up that number. It really varies. And DeepSeek did this right based on open source. Well, there's two interesting trends and probably the researchers will sort of win at my simplification. You have distillation on one hand, which can take a very expensive model and make something that's almost as good but much cheaper for inference.

34:15And then you have this post training process, which is reinforcement learning on chains of thought, which is the basis of things like O3 and O4 and these really advanced models. The two together has just, you just had so many variables of like cost, performance, quality, reasoning, all these other things. And actually, the reason I think it's exciting is if you look at like the database space right now, there's not one database. If you want to do large -scale data analytics, you'll choose one thing. If you want to do a transactional data story, you'll do another. I think we're moving to that area of models.

34:45And like when Clay and I talk about how to produce like a delightful low latency phone conversation, you care a lot about latency. That's one metric of quality that isn't intelligence, but it's important and put another way. If you had to think for 30 seconds before responding on a phone call, that might not be a viable. Then similarly, if let's say you're a company who has a relatively inexpensive offshore contact center and you need your cost of your AI to be lower than that, well, cost matters to, because $2 ,000 per phone call, it might not actually be viable as a business. So I say all that because I'm not sure there's one answer to your question, Ben, because it really depends on like who you're selling it to and what their goals are.

35:29But I think that's good because sometimes you want something for drug discovery and you want the most intelligent model. Sometimes you want something for a low latency transactional phone call and you care about latency and cost more. It's creating a market for these things where I think if you're one of these big foundation model companies, you actually can have a portfolio of products. But to your point, like broadly, like first principles, I think the reason why there's, you know, Clay mentions the consolidation in this space, it has to be consolidated because to basically make the money back on the pre -training and post -training process, you need relatively few players that are collecting taxes, you know, from all the players on top or you just can't make the math work.

36:11Yeah, first start -ups. going it on your own is just completely non -viable on every dimension because there's the CapEx, but there's also the OPEX. You could spend the CapEx. Let's say you magically got $50 billion as a startup to build a frontier model. Well, you can't just let it sit there, right? Operating that model and having it continue to run and continue to improve on it, that's what people are getting paid $300 million a year to do. The primary constraint I don't think is capital that starts with the people. And there's a small set of people who know how to architect these models, do the pre -training runs, do post -training, RL runs, and so that would be the starting place.

36:52And then to your point, the capital outlay for building a data center that can train these multi -trillion parameter account models is just enormous. And you have to amortize the cost of the people, amortize the cost of the capital to build out the data centers and then do that to your point in a pretty short period of time in order to make the math work. And so I do think there will be a very small number of these frontier models and research labs producing them. They will optimize all the way down to the memory, the chips, power delivery, and build this highly vertically integrated stack to get as much value out of the model as quickly as possible and at lowest cost possible.

37:36And by the way though, just with all the press around the talent, it's still a round -earned era compared to the infrastructure. So I think it's worth keeping that in mind. It's just very expensive period just because it's a salacious story. The CapEx is still the dominant cost. Just earlier today in Google earnings, right? This is like, oh, we're going to spend an extra $10 billion on infrastructure build -out this year. Going from like 75 to 85. Something like that. 75 to 85. I was like, oh, by the way, we're spending another 10. That's the scale at which these buildouts are occurring. Which is so interesting because these tech businesses that everyone loved and applied these really high multiples to for so long were these asset light low -capac's requirement businesses where your expensive thing was your human capital to produce software.

38:25And then once you've paid for your human capital, you just have this 85 % gross margin, amazing business model of software, that's not really true anymore. These big tech companies have massive ongoing capex. It is. I mean, it probably cuts both ways too. I think it represents significant barriers to entry too. And, you know, I think just look at Amazon Web Services, which is like one of the more impressive businesses built over the past, you know, 30 years. And in contrast, if you look at the trends of like how often do you see a new social service pretty often, right? like over that 30 year period, it doesn't mean that products like Facebook have gone away, but it's the barriers to entry are much lower as well.

39:06So, do you write them that like the the way you model these businesses changes is a significant barrier to entry as well. And so, I'm not sure how to think about it strategically because you can look at like a DCF analysis in one way. I think it's important to zoom out. The half -life of technology companies is not that long. There are a few, but they're the exception. And I brought this story up before, this 100th century. I started at Google over in the small building in Mountain View and we moved into a campus and it was the Silicon Graphics campus. Amusingly, by the way, they were still using a couple of buildings on the end.

39:40So you had this company that was literally dying and selling their campus for parts. I didn't realize SGI was still there, all that must've been so sad. Oh, we go into the cafeteria and we had free food and they were paying for the refute in the same cafeteria. I mean, it was just super awkward. Oh, brutal. And then though at Facebook, we were at this first the office downtown, then the old HP building next to Stanford, and then we moved into Sun Microsystems campus, which had also died. So like both of those companies in my lifetime, were at the top of the stop market, and then had sold their campus for parts, and then we were taking it over.

40:14So I say all that because it's useful to look at like 84 % margins above a lot, but like how many technology companies have lasted more than 40 years? and obviously these technologies are new. So I think it's just complicated as you look at these things. I would make the same decisions as a lot of the hyperscillars in terms of CAPEX. I think first, the promise of the value of artificial general intelligence is so great. I think it is worth the expected value equation is absolutely worth it in my opinion. And then similarly, I do think the scale afforded by these investments represents a lot of strategic values.

40:51Well, I totally get why it's complicated for investors, but I think it's just the landscape changes, and I think there's probably a bigger risk of not existing in 30 years than selling a spreadsheet. Well, if you look at their market caps, investors are giving them a pass for now. Speaking of strategy, you guys have talked about this plenty elsewhere, but I really want to double -click with you your business model, and pricing strategy, et cetera, is radical. Brett, you were most recently the co -CEO of Salesforce. I could not imagine a more deeply like invented software as a service category.

41:25And so you guys making the decision to throw out that business model and do something else for enterprise software is radical. It's very telling at the very least, lay it on us, what's the strategy and why you do it? Well, we started from first principles and asked what are agents actually doing? And in contrast to software as a service or software you'd buy off a shelf that fries electronics decades ago, which might help you be marginally more productive, help you get a job done. Agents in contrast are actually getting the job done for you. And so, you're in essence hiring software to accomplish a task and get it done well.

42:06And so, as we were thinking about, how do you price this? What is the business model seat -based? Well, what is a seat? That doesn't make any sense. Consumption? Is it per message? Is it per token? Is it per conversation? And none of these things actually mapped very well to getting a job done and getting a job done well. And so going to kind of principles of value -based pricing and pricing against value -delivered, we arrived at what we call outcome -based pricing or resolution -based pricing where we only charge our customers when their agent successfully completes the task that it's set out to do.

42:47So, you know, the ADT case. And that's defined as human does not get involved. That's correct. So completely gets the job done. And the reason this is important is what we're trying to do in a way is resolve this age -old tension between the cost and quality of customer experience where I think every great business wants to deliver an amazing experience to their customers. But unless you're like Hermes or the four seasons, it's too expensive to do. A phone call might cost 10 or 15 or $20 or rolling a truck. A service truck might cost hundreds of dollars. And so how do you bridge that gap? And we think that AI changes that.

43:26And AI, it turns out, can do things that people value very much. It can reason and decide. It can take action. It can speak your language. It never gets tired, it's always patient. And most importantly, it can get this job done. And what we love about the model is it deeply aligns with our incentives with our customers. They want to lean on their Sierra agent as much as possible because when they do, we deliver a better customer experience and save the money. We're motivated to build the most performant, capable agents that we can. So it ends up a very different relationship where as opposed to kind of vendor customer, we are partners trying to build this incredible agent to elevate the customer experience and also save cost.

44:12Well, you guys are effectively carrying the risk. You're incurring all these costs of doing all this AI, of building all this software, of hiring all these people, but you're not getting paid unless the ultimate bottom of funnel thing happens. That's right. It's an expression of confidence that our platform will deliver and our agents will be the best and that they can deliver. And so again, I think it's a strong signal of the quality that we can deliver our confidence and the technology. And again, the incentives alignment is extremely powerful. If you talk to like a CompExpert, if you look at like, take an enterprise software sales team, usually their compensation is 50 % salary, 50 % performance based on quota attainment.

44:56With people, we've always thought about like how do you incentivize the behaviors you want and it's a huge topic and executive compensation for different roles. We really just want to move software in that direction. So if the AI agent is supposed to make a sale, it should be paid a commission. If the AI agent is supposed to handle customer service, it should be paid when it solves the problem. If it doesn't, it didn't do a job. If we didn't do anything valuable for you, you shouldn't pay for it. To your point Ben, we're taking on the risk. But I also think as a consequence, we're making something more valuable.

45:27You know, it's very easy for our customers to know the value of their Sierra agent. They know how much it would have cost to have a phone call with a person. They know how much it costs to have the Sierra agent of Solvitt. We're saving our customers hundreds of millions of dollars in operating expenses and improving their customer experience. And your point, Dave, when you brought up the software's service revolution in the early 2000s, I'll pause so you can take this in your direction you want, but I think this will upend the business model of enterprise software in a really positive way. Oh, I still want to ask about this.

45:58There's the analogy with ads, online ads, where we move from CPM to CPC to paper conversion. And as you move closer and closer to the actual value, you can deliver a much more valuable service. And you have to. It forces your company to deliver value. Otherwise you go out of business. Have you guys started to see or discover little glimpses? I mean, really don't discover radical and bring a brand you said. like if this model takes, it's gonna change everything. This isn't just like a pricing model. When Mark Benioff invented SaaS, you know, it changed everything about Silicon Valley, not just how software is delivered.

46:37Have you guys started to see your get inklings of what the second and third order effects of the business model is gonna be? Clay, I've mentioned that we're more partner than vendor. I'm gonna give my historical context, which might be slightly embellished, but you can poke at it. So if you look at perpetual license software where you bought a version of a piece of software. What you do is a company would buy it, and then typically you'd have a group of people at the company who installed it and maintained it and managed the upgrade process and ran the servers and did a lot of stuff. When you had the equivalent of that that was software as a service, it wasn't just that you switched from CapEx to OpEx and you had a perpetual license and you went to a subscription software which changed the county and all that.

47:20It also changed the people you needed. You didn't really need the site reliability engineers to keep the service up because that was actually something that you got from your software as a service vendor. You didn't actually have to worry about the servers either. So you could actually probably get rid of a data center. Actually, if you switch entirely to, you know, software as a service, you probably don't even need a data center team at all. So you end up where not only do you change the way you pay for the software, but you actually change like the roles and responsibilities for it. With software as a service, you kind of like obviated the need for a lot of the lower level kind of technical machinery around in this software.

47:58But you still had to install it and customize it and all these other things. And I was usually an analogialist to take like a CRM software or something and you install it and your sales don't improve. Like well, who's fault is it? And I don't think many people would blame the CRM software. It's like maybe a bad sales people. I don't know. Are you talking from experience now? No, no, no, no, no, no, it's 100%. It's like, we use Salesforce to say, it's a great CRM, I love it. It's like, I'm very loyal in that place. It's more just like an accountability thing, right? Like it's, you know, the software vendor provides the software, it's up to you to make it work.

48:30And there's this like arms length accountability for it. As you said, Ben, we don't get paid if it doesn't work. So it actually like really changes the shape of what I think a software vendor would call like a post sales process. Because, you know, you can't just throw software on the wall and say, good luck to you. because it's actually in our interest to make you successful too. And so if you don't know how to make your agent work, we want to come help you do that. So we spend as much time thinking about how to be partners to our customers after they purchase our software as we do before. And we have customers who are extremely technically sophisticated, who don't even want to talk to us, that's great.

49:09We have some who barely have an IT team. And they help us, we want you to make it work, and we need to make sure that we match, sort of like meet those needs as they come up. I think that's exciting because I think the relationship that companies will have with Sierra will just be, will be as different as the difference in relationship that we went through with the on -premises, software's a service vendor. And I think it will mean, sort of as Clay said, you're hiring an agent to do a job. And that's just a very different relationship than installing a piece of software. And so I think it's exciting.

49:42And I think the second of four order effects will be how procurement teams think of what they expect of their software vendors towards outcomes accountability, as you said, then risk is fundamentally the role here. But I also hope that if we fast forward 10 years and maybe we have the privilege of being, you know, the you're doing an acquired on us and the magic of our business model is when you talk to our customers like, were there strategic advisor in AI? You know, it's like a deeper, more foundational relationship than simply a software vendor. Which is funny. I feel like the best enterprise software organizations and sales folks and leaders have always bitten at to their customers, but the business model wasn't aligned.

50:24They weren't really directly incentivized to. Yeah, ask a head of the camera like when you get the bill of materials. I'd be like, what is this stuff? Brett mentioned commission. We work with one very large furniture retailer. We're paid on commission if we attach a premium delivery service to furniture delivery. And so it was like, we're literally paid on sales commission today. And so it just broadens the aperture of what's possible with the software, what are all the jobs that a customer facing agent could do. And that's where we get really excited about the possibilities here. It's like, what will that look like when a company can show up and have added its best in every moment with its customers, with an agent that is fluent and helpful, and can actually get stuff done for you across kind of all parts of the customer life cycle.

51:10You know what's funny is we just used an old word that is already obsolete in your model, which is Post -Sale. You were talking about how, oh, Post -Sale, you know, we're incentivized to go and work with our customers. In fact, we demand it. We have to because it's our ability to, It's actually not post sale. You've signed a deal, but you haven't made the money. Right, sales don't happen until their sales happen. Right, so the whole notion of like there's this firm dividing line between pre sale and then the sale and then post sale kind of being about renewal. Ultimately what post sale is about is are we gonna get the renewal next year or in three years or whatever?

51:44This kind of breaks that. It does and actually at least to a couple of other things which is like two things, which is speed to delivery matters a ton and making it super, super easy to set up now there's a ton too. Because to your point Ben, if it's complicated or slow, you're not earning money until it's live and successful. So we focused on a couple of things. We're typically going live in a small handful of weeks. It can be as low as two or three weeks for agile firm. It can go as high as a couple of months for maybe more, I'll say, traditional company that has a lot of internal gates to go live.

52:20The other thing though is we spend a lot of time to enable not just technology teams to make these agents, but also their customer experience teams. One of the reasons why IT projects go slowly is you end up with this kind of slow loop of like figure out what the requirements are, throw them over the wall, have someone implement it. Go, no, that wasn't right. Go back and forth and do this loop. Talk separately to the marketing team. If you think about the furniture retailer Clay mentioned and like who's the expert in these premium delivery packages, what's been effective in the past, That's someone on the business team, not someone on the tech team.

52:54So we have all these no code tools. So these teams can go in and actually build their agents themselves, no AI expertise, no tech expertise. But all goes back to the thing you said, Ben, which is like, we need to empower customers to make these successful interactions live because it is a gate to our revenue model. But it aligns all these incentives. The reason why we're so focused on going live in days or weeks is because we're as interested in you in that being the case. And I really love the incentive alignment that it drives in our product. And in theory, it should open up way more experimentation for customers.

53:27If you don't have to sign a big contract and then be locked into that vendor and have a big implementation time and owe them a certain amount of money no matter what, it's like, OK, I'll adopt five vendors. And we'll see which one actually moves the needle for our business. Now that ignores the complexity of like, there is real set up time and there's human focus as your bottleneck. But if we can solve some of these problems, then in theory, companies should just adopt way more partners and see what works in the way that Cloud allowed you to just allow your engineers to quickly spin something up versus provisioning a server.

53:56It cuts both ways, too. I think one thing if you talk to a head of technology at a big firm right now, they've probably done too many proofs of concept and don't have enough live, successful. So it cuts both ways. It's fine to experiment if you have the wherewithal to make decisions and move quickly. When we advise our clients, we often say, like, you know, have the business metric or gonna go achieve and just go achieve it, you know, and running a lot of experiments can be useful, but often, you know, just having the like, top down initiative to do it is just as important. But then there's this other thing I think related to what you said.

54:33There's this term and enterprise software, best of platform or best of breed. Best of platform is sort of like that proverb no one gets fired for buying IBM. It's basically saying like, look, if you have a huge enterprise license agreement with one of the big incumbent vendors, Microsoft or whatever. And they have a new offering for an ERP system. Buying that, no one's gonna, your CEO is not gonna be like, you bought a ERP system for Microsoft, what are you crazy? No one will ever say that. So that's where I think procurement processes and purchasing processes are 10 towards platforms. And so the more a technology is considered a commodity, I think the more it tends towards best of platform because you get essentially commodities of scale.

55:17You can, you know, in your big enterprise license agreement, you can get better discounts, you can do all these things. You don't need to onboard a new vendor security blah, blah, blah. When new technologies come out, this pendulum swings from best of platform towards best of breed. And the reason for that is, incumbents typically aren't that great at these new technologies. We talked about business model changes. As if your software is a service vendor, you kind of have a strategic impediment to embracing new business models. Similarly, just because you're good at making a database in the cloud for ITSM system doesn't mean you're necessarily good at making AI agents, right?

55:51So there's technology barriers, there's business model barriers. And so right now, I think to your point Ben, people are experimenting a lot more, but also bluntly put, the value of the AI agent in displacing labor costs is so much greater than the software costs that people will go towards the highest quality software right now, which which is in companies like Sierra. I don't think that will happen forever. At some point, it will be talking on this podcast and it's like, oh yeah, agents, I made one, I made 12 this weekend. It's like no, it's no longer technically hard. And then you start to swing back towards platforms.

56:25And this is the race. So right now, there are best of breed companies like Sierra. Can we gain enough of a clientele and customer base and customer success that in 10 years, we are the incumbent? or will we not prove our value to enough people such that when the best practices of these technologies become more commonplace that incumbents can adopt it. And you see this time and time again, I think it's why almost every grade technology company was born in a period of technology disruption. The internet gave birth to everything from Salesforce to Google to Amazon. The mobile phone gave WhatsApp, Uber, DoorDash, Instacart.

57:05And so right now it's like, I just look at all these like saplings that are growing right now and like which of them will grow into the next generation. So anyway, that's how I think about it for what's worth is like it's like a race. Right now quality is all that matters and it's why you know our company's going so well, but it is not something we're entitled to for a decade. We need to essentially create the kind of scale that is necessary as this technology becomes commonplace. So you've both led really big teams and you both have been sort of the like crack incubation project in the past. Brad, I'm thinking Google Maps or Clay, you know, recently with Project Starline, which is now Google Beam.

57:47Is that the right one? Google Beam, yeah, Beamy Upscitey. Sweet. In building Sierra, in this AI era, is there anything different about being leaders of people and leaders of teams versus sort of these almost famous teams that you've led in in the past. I think first and foremost, in building a startup, you operate just at an entirely different scale. Like orders of magnitude, smaller scale than Bret and I were operating at in certainly our most recent jobs. And so there's a proximity to all of the details, all of the work that is important in building and running the business, and that I think is part of leadership.

58:24And demonstrating, it was like, look, we're in a very small boat together, and out to build something great. Brett will, on a Sunday night, check in, you know, a thousand lines of pristine code, and I will be in the weeds of pricing proposals and our contracts and the exact copy and our marketing language and so on. So I think first and foremost, it's just a level of being in the details. I think one thing we've tried to bring to Sierra that I think Echo's running these larger teams with broader sets of functions is when you're operating at the kind of intersection between what is possible and what is not yet possible, this kind of zone of the barely doable, it's super important that to the largest extent possible you be able to kind of control your own technology destiny and so we talked earlier, we don't do our own pre -training, we don't build our own foundation models.

59:19We do have a small research team and I think that's somewhat uncommon for an applied application layer company, but many of the breakthroughs that we've had that have enabled us to deliver such quality and cost savings and more have come through novel agent architectures and really going down a click or two in the stack to innovate at lower levels of the technology stack. Has that felt familiar to you because it seems like your whole career has kind of been in frontier technologies. If you look at all the AR and VR and very much so. So that's actually one of the parts I love most about building Sierra is you are at this frontier where it's like can we even make this work?

1:00:03Can we get this thing to do this thing reliably and well? And so it's not a simple matter of programming. It's not typing into a keyboard or I guess asking cursor to do something until a piece of software emerges. It's exploration, it's discovery, it's posing hypotheses and validating or invalidating those again and again and again. And so there's a real element of science and exploration and figuring out how to make this thing work. In addition to then translating those inventions into things that are directly useful for our customers, Contrasting what we do today with augmented reality glasses, the development cycle for a waveguide or a display.

1:00:47It's like years, or maybe just under a decade, what I love about this is the immediacy. We can have a breakthrough in our agent architecture on Monday, implemented on Tuesday, and have it deployed with hundreds of our customers on Wednesday and directly see the impact of that work. I love the immediacy of that and to have both this kind of invention and discovery and the unknown which is very exciting to be in and the direct you know practical application of it It's super fun and and one of the best parts of building the company Brett how's the leadership felt different for you this time around versus salesforers or Facebook or so plus one to everything place I think I think creating a company in the age of AI is interesting because we talked about how software engineering is impacted by AI, but everyone's job is as well.

1:01:40So I think one thing culturally that feels meaningful is having active conversations about how to use AI to do our jobs differently. It's an awkward conversation, but it's like, if you're a software engineer and you're not using something like cursor to do your job, you're probably being half as productive or even worse, then you could be. And so there's almost this, like you want people to sort of adopt these tools because they want to and you sort of need to sort of voluntel them to do it. It's like, I don't think we can succeed as a company if we're not the poster child for automation and everything that we do.

1:02:16And that feels really different. And I have a lot of empathy because I'll just take like a real simple example, like Salesforce that 80 ,000 employees after the pandemic getting people back to the office was a total pain in the ass. You know, like just people at Mood, people at this lifestyle changes all these things. Every big company goes through it. And people who say it's easy, like, haven't run any of these person company. You know, it's like, and different people have different approaches. It's just hard. At Sierra, we're in the office company. We just said, if you don't want to be in the office, don't work here.

1:02:43It's super easy. Like we're a new company. So it's just so easy to do these things at a small scale. I observe just like having everyone in our company, you know, you didn't use chat GPT deep research before your sales meeting? Are you kidding me? That's the best practice that everyone should do. Imagine doing that with 10 ,000 salespeople to roll that out. So I think about it a lot and then just having the vantage point of having come from larger. I just have a ton of empathy for lack of a better, like the cultural change management of absorbing these technologies into larger organizations. So we're trying to be the poster child of it.

1:03:23And then because we are a partner to so many larger firms. I have a lot of empathy for the challenges of adopting technology into cultures. I think it's really, really hard and I have a ton of respect for the leaders who are able to do it at a larger scale. I'm curious, maybe, as a good final question for you guys on this front about you guys as co -founders. I imagine that must have been extremely intentional because it's not like either of you giving your careers couldn't have just gone and built a company yourself, probably funded it yourself. You didn't need like the team slide to raise money.

1:03:56So to speak, you know, having you both on there. Or better put, you could have only had the team slide. Yeah, for either of you, it would have suffice. I must have been very intentional. How did you guys think about it? I've been trying to work with Clay unsuccessfully every single day since I left Google in 2007. This was 20 years in the making. The short version of this is the only way I could convince Clay to actually work with me to start a company with him. So I was like, fine, I'll do it. I'm just kidding. It was sort of like that though. So we started in the same program, Marissa Meyer, hairdos, both at Google associate product managers.

1:04:30We were more or less friends ever since. It was like a relatively small group of people. Legendary program. Oh, totally. And we had this monthly poker game that happened roughly twice a year, just because people were busy. But so we've been friends for a while. And every single place I went I would call Clay. I'd be like, you gotta come here, it's great. Because like, Sundar has this high of opinion as Clay as I do and it was just like hard to, you know, make everything work. And so we had lots of dinners and Clay may have a different version of this, but I'm just like, I just kept thinking to reject it.

1:05:01And so then what I said I was leaving Salesforce, we ended up having this long lunch and we both found out we shared a passion for large language models. What year was this? So it was December 2022. Okay, so Chatchy PT had just come out. Just come out. I had announced those even sales words. Chatchy V comes out like a week later and we're all just talking about it. And I was like, I didn't know what I was going to do, but now I know I'm going to work on this. I don't know what yet. You thought I was kind of the ARVR guy, which I was, but also in labs, I had been obsessed with language models and things like notebook LM, which came out of it.

1:05:34And we were both like, okay, we were both obsessed with what is unfolding right now in technology and where this goes. and over that lunch hatch plans to start the company together. We had no idea what it would do. We figured it out much later than like March because you got to get out of your job, do all these things. We just knew we just had the premise which is this technology is going to change everything. It's going to create a bunch of business opportunities. Let's go right into the darkness and figure it out later. But just a meta point, I'm just a huge believer in the power of partnership.

1:06:05I mean, you've interviewed a lot of entrepreneurs. It's hard. It's like stressful. You take everything personally. It is so nice to have a partner to do it with because when you're having the moment and you need to just like rant at the sky, you can go, whatever we call each other up, I just couldn't imagine doing it solo. I just don't. Well, it's funny. Part of the reason I asked the question, I didn't want to lead the witness too much. But when we talked about it at the beginning of our Google episode, the vast majority of companies we cover is the singular founder, the Mark Zuckerberg, the, it may even Microsoft like, you know, Bill had Paul Allen.

1:06:42They have co -founders, but they're not the main. The main guy. You guys grew up at Google, which was like a true partnership. Yeah, I was just wondering if that, you know, formative experience in seeing Larian Sergey together rubbed off on you a little bit. It's actually funny to say that too, because Clay and Bretton Clay has the Larian Sergey. Like, people talk about us as a unit. They joke around that we spend way too much time together. So... So much time together. Instead of having a holiday party, We have a Sierra birthday party every year in March and Brett and I said a few remarks and someone said You guys seem to have a really nice dynamic.

1:07:18This was one of the spouses there And I said yo, it helps that we actually like each other and like spending time together The other funny thing is like I'm not sure which of you made the better decision after your APM Stint of like Brett you obviously created a lot of market cap where you went Clay you you also like by not going anywhere created a lot of market cap. Yeah, it turns out both Facebook and Google are pretty good companies. Brett, in many ways I feel like you have the single best career of anyone in Silicon Valley in the last 50 years. Like, do you ever like reflect on that and pinch yourself and go, how the hell did this happen?

1:07:54Well, that's very kind of you. The thing actually I feel most grateful for is to have been inside of some of these remarkable companies. There is a parallel to actually the acquired podcast. What I've always loved about listing to your overviews of companies is the genuine sort of like affection for the companies and business models and what makes them great and what makes them tick. Like it exudes some way you talk about these companies and I feel that way about Google and Salesforce and Facebook and my own companies that I've started because they're also different yet they're all successful and you know I remember first going into like a Salesforce management team meeting and be like I don't understand anything going in audio.

1:08:35It was just so different than, you know, certainly equipped to come in. I'd started that like Facebook and Google, yet it was this remarkably successful company. So I was like an anthropologist, you know, it was like Jane Goodall observing the guerrillas or something like, what is going on here? I'm like, take a note. So I'm like, so when he says this, this person does that and why is that good? I need to figure this out. And so you end up realizing just like the shape of consumer companies and enterprise companies and I thought I knew what great go -to -market looked like until I went to Salesforce and realized that I had just simply never seen greatness before.

1:09:10I just feel like it's been such a privilege to learn from people like Marissa and Larry and Sergey and spend a lot of time with Mark Zuckerberg and Mark Benioff is one of the closest mentors I've had in business. So yeah, the resume, whatever, it's in the very kind words you said, but actually for me, just having been there and actually gotten to see what you all cover every day, but first person and actually contribute to what a privilege. So it's been a fun, just to observe some of the great companies of Silicon Valley. And meanwhile, Clay, you got to know the absolute crap out of Google. Well, Brett is doing all that.

1:09:4518 years, is that right? Over 18 years, I worked on basically every part of the company. Search and ads and then ran product and design for workspace. And kind of played an enterprise software person on TV for a couple of years, because it was both the consumer applications and then Google Apps for work. And then there was that awkward period of G Suite before it was workspace, a name I much prefer. And then spent most of the last 10 years working for Sundar, building forward -looking things for the company, AR and VR. Google Lens, one of the earlier applications of applied AI, and then most recently rehydrating Google Labs, at least the name, as kind of incubator of forward looking bats for the company.

1:10:30And that's where things like AI Studio and Notebook LM and some of the more recent AI applications came out of touching on a similar threat as Brett. I just feel such gratitude to have seen greatness up close to have been some small part of building the company. And to have had within 18 years of Google on a way, I don't know, two, three, four different careers or jobs, where I built hardware from scratch and visited assembly lines in China to see headsets and wearables being assembled. And that was something that when I joined in 2005 as an APM working on some part of the ad system, never would have occurred to me.

1:11:15And so the flexibility, the opportunity, and the privilege of operating with such scale, building something and having it in the hands of hundreds of millions, if not billions of people, it's truly something. I love my time there and I'm immensely grateful for everything I learned and most of all the friends and just amazing colleagues I made along the way. All right, wait, I got one more question before we wrap it. I can't let this be friend lunches, poker games during the Google Plus era. What was your conversation like then, you know, Brett, did you know you were going to win? We've woven it out like when I started Quip Clay was working on Google Apps, so we were still cordial, you know, we did talk shop very much during it.

1:12:03He was a giant and Quip was so small, I mean at that time it was like, sorry, sorry. It was the notion of its time. And this has been strategy. He built you up and then he cuts you down. And it was actually our poker like Lars Rasmussen. He was one of the guys who created Google Maps with me. Also went to Facebook and was part of our poker circle too. And so we've mixed a lot and actually a testament to like relationships being deeper than rivalry in some of these places. It was still very fun. We give each other a little shit. So it was fun. My favorite year is that Google were definitely not the Google Plus year.

1:12:37So I'll just say that. Yeah, Brett, you probably weren't even allowed out of the building to go play poker because it is. You know, code route down, right? Yeah. This is something that I think is totally lost to history unless you guys lived it like you. At Facebook, it was an existential threat. We are so scared that Google is actually going to get this right. And at Google, the, the Earth's Quake memo, I mean, Google for three years completely reoriented priorities as a company saying we have to nail social. It wasn't just like a side thing for either company. This was like the battlefield and it ended up actually being a nothing burger, but at the time, it really mattered to both sides.

1:13:16It did. I mean, this is a thing, like this is what's going on with AI right now too. I mean, when smart people at all these companies realize the size of these markets and, you know, at the time, how will sharing within these social graphs and private networks impact the net and search and all these other things. Like, it feels existential on all sides. And, you know, I think it's easier to trivialize. It's very easy to make Google plus jokes. But it was like a genuine effort and easy for you to make. Yeah, certainly. So it's like, you know, and I've certainly had my mistakes in the past. We wanted to go through this all today.

1:13:54I think there are a lot of parallels though, because when you have a technology incumbent faced with a big new wave of technology, Microsoft famously, I think, fumbled on mobile, despite Windows phone and Windows mobile being ahead of many of the other operating systems at one point, did very well in cloud. But both were treated with a lot of gravity at that company, and right now, just that analogy, part of it was born of the personal rivalries and the staff weaving between Facebook and Google, which was somewhat unique to that time. Put another way, I think, I joke, There's like a corporate strategy and then there's like pure ego and I think there's a mix of a lot of the two But I think you can see the same thing in AI right now Everyone's trying to recognize this wave of technology is gonna dramatically change markets And what do we want to be when we grow up and you're gonna see the equivalent of worst quake I had a lot of different companies right now given the wave of AI well Brett clay.

1:14:50Thank you so much for coming on with us Thanks for having us. Thank you so much for having us listeners. We'll see you next time. We'll see you next time