Bret Taylor of Sierra on AI agents, outcome-based pricing, and the OpenAI board

19:49Bret Taylor:That's actually like that you're not compatible with them. And I'm not sure that's right. So I think it's great that these things are backwards compatible. It's great that our agents have spoken over the telephone already in English. That's really funny. But I don't think it's like the long-term future because there's so much value that you can provide. Put another way, the agents using a sophisticated application harness can just do a lot more and do a lot more like with higher fidelity.

20:14John:Yes. Well, we should get to Sierra because you see a lot of real-world AI adoption. And so maybe start by grounding us. What is Sierra? The business has scaled very quickly. So what are the latest metrics that you can share? Because they keep changing from month to month. Yeah.

20:30Bret Taylor:So Sierra, we help companies make AI agents for their customer experience. So if you have a big phone line, these agents can replace your IVR system and just pick up the phone. If you have a digital chat system, an AI agent can pick it up. You don't need to wait on hold. These agents can not only answer questions, but take action on your behalf. We work with healthcare companies like Cigna. We just did a great case study with SoFi. And I'm really proud that we raised their net promoter score by 33 points just because it's just so delightful. It's really fun to see all these different brands across a wide range of industries get so much value from their agent.

21:10Bret Taylor:With a leader in the space, like you talked about the metrics, we reached$100 million in ARR in seven quarters, 150 in eight quarters. We're, I think, around 165 now, one month into our next quarter. So growing really rapidly and really proud of the momentum that we have.

21:27John:That's super cool. What is the typical adoption? Are people using it for email chat support because that's the easiest modality? Do they adopt it for everything, including phone and stuff like that?

21:39Bret Taylor:It's changed a lot over the past two years, but I'll say the median customer, and they'll describe some interesting outliers, which I hope are sort of gumsens of the future. So most will start with one channel and a few use cases. So at a lot of healthcare companies, phone remains the dominant channel. So say, hey, for a few types of phone calls, let's have the AI agent take them and see how it does. Do people like it? Are people comfortable with it? Does it lower our cost? Does it raise whatever metrics? Usually it's customer satisfaction. and does it work more effectively? So, for example, like for a car insurance company, it'll be like first notice of loss.

22:16Bret Taylor:You know, I got into Fender Bender, you know, and that would be the typical way you start. For a lot of more digitally native companies, they'll start with chat and kind of similar. But almost all of our clients will do both. So SiriusXM, if you call them on the phone, their AI agent, Harmony, which I love that name for SiriusXM, will pick up the phone. And if you go to their homepage and you see the chat, that's also the same agent. So the neat part is, I think it's pretty neat because you have like literally you have all of your, I'll say, customer experience team or, you know, whatever you might call it at your company.

22:49Bret Taylor:They can spend all their time on one thing. Yes. And it actually works over WhatsApp. It can work online. Yes. It can work in your website, work in mobile app. It can pick up the phone. That's a pretty big change. A lot of our clients, when we start working with them, they'll have like a digital team and a call center team and all these different teams. And we've kind of gotten to the point because we've digitized the last remaining analog channel, which is the telephone. Those are all unified. When I start by sort of glimpse of the future, we have a few like really ambitious customers like Rocket Mortgage, great Detroit company.

23:20Bret Taylor:They own Redfin. They bought a mortgage services company called Mr. Cooper. If you go to Redfin, you can search for a home using an AI agent. If you go to rocket.com, you can originate a mortgage with an AI agent and you can service that mortgage.

23:33John:It becomes about product usage rather than just customer service.

23:35Bret Taylor:And really end-to-end, sales, service. And I think that's really exciting. I mean, our whole view is that if we're in 1994 and you were doing Cheeky Pint about this internet phenomenon.

23:47John:I don't mean a fan. I was a bit young, but yeah.

23:49Bret Taylor:Yeah, we would have both been. I don't have my Nirvana shirt on. You know, we would be talking about like, look, this is like your digital front door. Or maybe we wouldn't have the pressures to say that. On the information super high. On the information superhighway. And I think the same is true of most companies, AI agent singular. There are lots of agents, but the one with your brand at the top that your customers interact with is special. And that's the one we're trying to power for companies.

24:17John:So you think this, like what customer is built on Sierra, your aspiration is that it just becomes sometimes the primary way people deal with the company?

24:26Bret Taylor:I think a company's AI agent will be the vast majority of their digital interactions. And I think digital has come to include the telephone. And that's sort of a big shift because we think of that differently. And that's a huge change just because the bigger shift. So customer service, which is one big part of what we do, but not the only thing we do, is traditionally been thought of as a cost center because it's really expensive. So I'm sure you have people answering the phone for your clients. And depending on where they're located and how well trained they have to be, like how simple is the case, it can cost 10, $20.

25:02Bret Taylor:It can be much less if it's a more simple case. And you have some customers who pay you millions of dollars, and you'll answer the phone anytime they call, and you might have one that has not even started monetizing yet. And you might want to call them, but there's a limit to literally how much you can afford to talk to that person and still have a profitable business. I always joke, it's probably easier for you and me to call Sundar than to get Google Customer Service on the phone. It's very hard to get Google's calls.

25:30John:But it's not because they don't like you.

25:33Bret Taylor:It's just if you think about the average revenue per user of Google, they literally, I mean, they just can't afford to do it. So now if you take that$10 or$20 phone call and you make it$0.10 or$0.20 and over time$0.01 to$0.02, all of a sudden not only can you afford to provide a great customer experience to more people, even less profitable customers or in lower margin businesses, which I think is very exciting. So it's like not just doing what you did before but new. You can provide better customer services. You really can. And then just think about running like a subscription business where you care as much about customer acquisition and you care a lot about churn because that's how your lifetime value equation works.

26:12Bret Taylor:And you think about, okay, if I had a budget of how much I spent on service and now I can do 100 conversations more than I could before, can I actually reduce my churn rate? Can I improve lifetime value? And then the interesting thing is then you realize that, wow, all of my competitors have access to the same technology. Yes, yes. And then you're saying, okay, what are my competitors going to do to actually take my customers away from me? And then that's when you start to get things like, you know, the ATM machine didn't actually reduce bank branches because some bank had the great idea of, I'm going to put different people in this branch.

26:46Bret Taylor:It'll generate revenue. And all of a sudden, it wasn't job displacement, but something completely different. So I think the exciting part in our world is you're taking something that's just so, so, so expensive that people literally hid their phone numbers so people couldn't call them. And you're making it inexpensive and delightful. And the thing I'm excited about is the second and third order effects are going to be really interesting and very hard to predict. And that's pretty exciting.

27:11John:I want to come back to this idea of the agent as the UI because I found it really interesting. We talked about this a bit in your letter in the context of agentic commerce. where again, I think people are trying to pitch too much of the end state of like, you know, fully autonomous, you know, the robots just choosing few. And the point we always make is like, let's just start with not having to fill out the web form. Like no one likes filling out forms on the internet. Speak for yourself. I wonder just, will using websites have been actually a bit of a, like the fax machine, you know, we used to hum emails over the telephone lines as a way of transmitting information or like i wasn't working for this but like there was an era of like voicemail memos where you're in the working world for that some people still

27:53Bret Taylor:do this where they you know do like companies will like blast a voicemail memo to like employees at

27:59John:the company and like that's a way of distributing information and all these things are like very moment in time and maybe navigating websites and filling out forms was like a bit of a moment in time is that how you see things playing out i don't know i mean it's interesting because if you look at the past few iterations of technology, you had the PC revolution, then you had the

28:19Bret Taylor:internet and the browser, then the smartphone came out and the tablet. And I was more optimistic about tablets than sort of the way the world turned out. You know, I see more tablets on airplanes, but like, I don't, you know, I'm guessing if I walked around Stripe, I would

28:34John:see very few tablets out.

28:36Bret Taylor:And similarly, there's more smartphones than people, but there's still about 2 billion PCs in the world. I think it peaked some number of years ago, but it hasn't gone down as far as I know. And I haven't tracked this. That's interesting, right? We sort of added to our digital world. But I think perhaps the more interesting metric is for like you and me, what percentage of emails were sent through each device? And certainly from 2010 to 2020, most of the world might have transitioned from like percentage of email on desktop to smartphone significantly. And so it's almost like a market share of digital interactions, which I think is a really interesting way to think about it.

29:17Bret Taylor:And certainly as you think of like Stripe's business, like where does commerce originate? And you saw that move to mobile, but it doesn't mean that people, it's actually a very big, you wouldn't want to eliminate the PC commerce business. Like that would actually be catastrophically bad. And so then you look at AI agents and I believe most businesses it will be their primary digital interface. And it's because it works over WhatsApp and it works over the phone. If smart speakers make a comeback, they'll work over smart speakers. Which they may now.

29:46John:Like maybe smart speakers were just too early.

29:48Bret Taylor:Yeah, well, it's like ask for the weather just turns out to be like not the biggest market in the world. But now -

29:53John:Set a timer. Set a timer.

29:55Bret Taylor:I mean, it's amazing they made that much money off a timer setting speaker. And so like it is very future-proofed because it's fundamentally conversational. but maybe it's like going from you know punch bar punch cards mice and keyboards touch screens now voice and chat and and probably 3d immersive at some point does it just sort of add and make the other ones less important uh is probably the way i think about it i do wonder if we'll see the end of the smartphone at some point it doesn't seem anywhere close to right now um but it is interesting i mean i think most people don't love how much we're sort of addicted to staring at this glowing screen.

30:34Bret Taylor:On the other hand, you can't talk to TikTok. It's fundamentally visual. But I wonder if there's a world where you could actually be really productive without such an invasive device on your body. And if that's the case, can it offer an opportunity to sort of like unwedge some of the addictive properties of these technologies and get a lot of the benefits from it? Because at least for me, I think all of us are so connected. You sort of end up like, I'm going to check my email.

31:01John:And then you're like, where have I been for the past hour?

31:05Bret Taylor:And the fact that we actually have technology that affords that kind of innovation now, I think that's quite interesting. So I don't know what the future is, but I'm very excited for it. I know it sounds really cheesy, but we've now changed the ingredients available and we have a lot more recipes we can cook. And I think that's very exciting.

31:22John:Yeah. I agree that I'm excited for not having to look at the screen for as many things for a variety of reasons. When a customer installs Sierra, I know there's a significant customer satisfaction component as well as cost, but I'm curious what kind of cost difference does it make? And maybe relatedly, when a customer is fully deployed, what kind of mix do they see between queries fully resolved agentically, things that end up having a human who is presumably somewhat AI-assisted? But just what does a normal equilibrium look like there?

31:57Bret Taylor:Yeah, it turns out most of our clients have pretty different priorities. So some are very focused on cost savings. And you can automate very, very high percentages of your cases. There's a company called Ramp that's a really impressive tech firm.

32:14John:We had Eric just here. Oh, that's great.

32:16Bret Taylor:Well, they're automating 90 % of their cases. They're really sophisticated, though, because they're basically getting in front of cases before they escalate. but I think it's kind of an example of just a really fantastic company, you know, implemented really well. And you can see anywhere between, you know, 70%, 90%, which is really incredible. The interesting thing, though, is there's counterintuitive effects to it. The cases that do make its way to your customer service team can end up more complex, sort of by definition. So what's called average handle time will actually go up. Yes. And we heard from one of our clients that actually their satisfaction of their call center agents went way up too, because it turns out it's way more fulfilling to solve a hard problem.

33:01Bret Taylor:Like, have you tried plugging it out and plugging it back in again? Exactly. The other issue when we had one retailer whose volume, total volume, went up almost as much as they saved from the AI agent. Gentleman's paradox. It was a form of that. So, you know, if you've used a chatbot from three years ago, they were really annoying. Like, three years ago, if you said, like, do you like chatbots? There was like zero people would say yes.

33:29John:It's so funny that there was a Silicon Valley wave of hype around chatbots. It was even earlier than that. It was like 2018. It was like pre-LLMs, pre-Transformers. Yeah. And they were just like multiple choice machines or something.

33:39Bret Taylor:It was just the worst products of all time. And so replacing it with something that was like a delightful way. People are like, I'm going to talk to this thing a lot more. So they ended up keeping their cost didn't really go down, but the volume of customer conversations went up two or three X. And the CEO was incredibly happy about it. They were like, we're now actually listening to our clients. So it sounds funny, but it's a little bit of a choice how much you want to drive cost savings with AI versus other metrics. Most of our clients are interested in the top line metrics. And so if you could save$10 or save$1 and improve your net promoter score and competitive positioning by a meaningful amount, everyone in the world would choose the latter.

34:27Bret Taylor:So that's the interesting thing going on right now because, again, going back to about – it was 1994 and we're hawking websites on this podcast. I think if we were to go to a major bank and say, if you launch a website, you're going to have a competitive advantage against every other bank. With the benefit of hindsight, that would have been overpromising. The correct thing to say was, if you don't want to launch a website. You have to have a website. And so this technology is broadly available. And so as a consequence, you can't just sort of like launch in all parts of AI, not just Arbus. You can't just launch AI, absorb the cost savings, pass it on to shareholders, unless you have a monopoly.

35:07Bret Taylor:Yes, yes.

35:09John:In most businesses, it's a consumer surplus.

35:11Bret Taylor:Exactly. So you're either going to lower prices, but I think that's why it's an overused analogy, but the ATM bank branch thing is really interesting because if every single company in an industry has access to technology, I would say it's an imperative, not a competitive advantage. Yes. And the more interesting, I would say, board discussion is when everyone adopts the obvious things. customer experience, customer service, software engineering, legal, just pick the ones where there's solutions, off the shelf solutions available now, what will the industry look like? And my guess is you could ask how GPT think, and my guess is there's some really interesting second-order effects.

35:51Bret Taylor:And when you have competitive markets, you're going to end up investing, lowering prices, whatever it may be. And that's the thing I don't think it's talked about enough. And I actually think that we just, it happens with every technology change. You project it through the lens of what you're doing today and you don't take an effect. It's like a multiplayer game that we're all in right now. And that's fascinating to me. And so the change is disruptive, but I think it's going to be like, I'm very excited for the next few years as like the world absorbs the technology. We start getting to some of those second and maybe even third order effects.

36:25John:What is the most impressive AI adoption or kind of AI native behavior you've seen from a client? Oh, that's a really good question.

36:34Bret Taylor:I'll probably say Rocket, where we have a really great relationship. I think Varun is their CEO, Sean Mahutra is their CTO. Two people who really are, I would say, not only just curious about AI, but very interested in transforming the home ownership experience with AI. And I don't know when you got your first mortgage, but it's very intimidating.

37:01John:And it's not a modern process. It's not a modern process.

37:05Bret Taylor:They literally call it mortgage folders for a reason. It used to be a folder. And for me, it's an example of a company trying to transform an industry. And the reason I brought it up in the context of our last question is it's not just saying, how can we take AI to do this? But it's like, if you were to think about the homeowner experience from searching for a home on Redfin all the way through servicing it, and you had AI available, what would the ideal experience be like? And so that's really interesting to see Rocket with their acquisition strategy too, kind of like integrate that experience.

37:39Bret Taylor:And that's what I'm excited about. I think there's an opportunity for CEOs and like industries like that to have a bold vision of like what the future could be. And going back to my point, imperative, not competitive advantage, it is a competitive advantage right now. So if you imagine, I haven't tracked the market share of all the big U.S. telcos, but if you look at T-Mobile, Verizon, AT &T, and you tracked it over the past 10 years, you end up with surges in market share growth. The iPhone came out. You ended up with 5G. And you end up with these things where, but it's my impression of the industry you end up with these moments that drive market share, and then it ends up at an equilibrium.

38:22Bret Taylor:So what's interesting about it is the iPhone moment for telecommunications companies like SoftBank in Japan. This is the moment where perhaps if you have a competitive equilibrium, you can absorb this technology, use it, and you'll have this window where you can actually shuffle the deck.

38:39John:It's a technology that shakes the competitive equilibrium. Yes, exactly right.

38:42Bret Taylor:You definitely notice that.

38:43John:Yeah. So you talked about how coding is such a domain that is suitable to AI, because all of the context you're working with exists in the repo. It is in text. It's kind of neatly organized to be executed and read by humans. And so there's kind of a good bounce there. the problems that customer service agents are not of that character. And so how do you actually smush everything into a format where your AI agent can answer it?

39:24Bret Taylor:Yeah, we spend a lot of time thinking about that. To some degree, one of our engineers called it almost like we're creating a domain-specific language for specifying customer experience. You know, like what is the mechanism of specifying it? We use this metaphor we call journeys, which is, you know, what is a customer journey end to end? And what does the agent need to be successful in that journey? What tools does it need to access? What information does it need to access? And you can, if you think about the capabilities of an agent like skills and a coding agent, you'll add different capabilities over time as the customer is talking to you.

40:05Bret Taylor:The key thing that's been a breakthrough that is probably not surprising to the technologists listening to this, but has been a huge difference between those crappy chatbots of four years ago is the reasoning capabilities. You know, I think the... We had one client who had acquired three companies, and they had three identity systems, three CRM systems, three of everything. And so they had this big IT project where they were going to unify all those systems. But I was like, why don't you just have the agent like go in all three of them and just think. And they're like, well, what if there's duplicate data?

40:38Bret Taylor:What if the data conflicts? And they're like, you know, that's going to fool. Sensor fusion.

40:42John:And I was like, well, what does your person, what does like the person do?

40:45Bret Taylor:Like, well, they kind of think about it. And I was like, oh, let's just do that. And that's the interesting thing about these AI agents is they actually, the basic humane, basic reasoning, not superhuman ASI, turns out to be the huge breakthrough in customer experience. The other interesting thing is the innate knowledge of the LLM. You don't want an AI agent to hallucinate, obviously. But Sonos is one of our clients. And do you have a Sonos speaker at your house? I have had, yeah, yeah. If a Sonos speaker ever breaks, it's never the speaker. It's always Wi-Fi. That's what I've learned. And it's always true of me, too, right?

41:22Bret Taylor:There's always some Wi-Fi. You know, if you wanted to make an AI agent to help you with your Sonos speaker, like you obviously can give it all the manuals for the speakers all the technical specifications you can give it the device telemetry all the stuff you need do you really need to give it the history of wi-fi well now it turns out like large language models have encountered every possible wi-fi problem so like why does the sonos ai work so effectively well it knows a lot about wi-fi in addition to all the sonos things and if you look for any given ai agent yes all of the like it turns out being trained on all of human knowledge is actually useful as a starting point for a lot of tasks.

41:59Bret Taylor:And I think that's been the big breakthrough. So how do you give it all of its knowledge? Well, first, we've built, I think, the best platform in the market to do so, where you can really narrow the guardrails for regulated conversations, widen them for less regulated conversations. But the fact it starts with knowledge of obscure Wi-Fi idiosyncrasies turns out to be the greatest

42:17John:breakthrough of all time. Have you had the opposite problem where there's a customer whose problem domains mostly don't exist on the public internet. You know, it's like we provide the drill bits used in, you know, deep sea oil drilling. And it just turns out there's nothing on Reddit about that. Yeah, so 100%.

42:33Bret Taylor:And, you know, we work with this like medical device company and it's a deep cut of human knowledge. You know, and you can train it all on that. In fact, we do a lot. One of the things you want to be really careful about if you have a really well-known brand, We work with, I want to say, a third of our clients have over$10 billion in revenue. Over half have over a billion revenue. So most of our clients are actually quite well-known. So one of the challenges when you're offering either sales or service or customer experience to a really well-known brand is it's harder to ground it. It's actually easier when the Internet has never heard of you and you want to make a well-grounded agent.

43:10Bret Taylor:It's actually pretty easy because there's no temptation from the LLMs to go off script. So actually, ironically, the harder challenge is when it's a very well-known brand, it's like, no, I got this. I'm like, no, you don't. You got to go look it up. You know, that's actually a harder problem.

43:25John:And so how do you force the LLMs, like mechanically, how do you force them to not, you know, answer off the top of their head, but actually look it up?

43:35Bret Taylor:So we use, we call it a constellation of models. So our platform, we call it Agent Studio, you essentially configure the goals and guardrails of a process. And goals and guardrails, not a sequence of steps, because you want agency, but you want guardrails around it. And within that, we'll use reasoning, but we use supervisor models to actually inspect that reasoning. And so if you were an AI agent in CIRA and you decided to go off script, like, I got this. What would end up happening is a supervisor agent would observe your reasoning, say, I think John should have actually looked up the policy here and send it back with notes and say, actually, you're not allowed to make that decision.

44:21Bret Taylor:Here's the reasons why. Go redo that decision. It's a really effective technique. The way I think about it, which is a little simplistic, but I think basically right. If you imagine a reasoning system is right 90 % of the time, but has some either guardrail malfunction or hallucination 10 % of the time, it's obviously better than that. And then you have a supervisor that's right 90 % of the time. If you chain them together, you get 99 % effectiveness. And so that methodology of layering reasoning and intelligence has been really effective. And in general, it sort of makes sense. you're basically layering compute, you're layering reasoning on top of it.

45:00Bret Taylor:What's neat about it, though, is we can sort of abstract that complexity from our clients. So, you know, they're sort of expressing the goals and guardrails, and we have all these evals and tests and all these other things. We can find ways to make it more and more and more robust over time, but it doesn't require you to, you know, prompt engineer, write in all caps or whatever, like the hacks that people use to get these things to be conformant.

45:23John:And you started in 22, 23?

45:28Bret Taylor:We launched the company on February 13th, two years ago. So I guess we're like... Oh, 24. Yeah, so our two-year birthday was like a couple weeks ago.

45:39John:What I was thinking as you were saying that is, did you sort of co-evolve Chain of Thoughts and RL and some of these things that are now in the models, but did you have to build your own kind of janky version of them before they were in the models?

45:51Bret Taylor:So, yes, and it's also a talk, which is the weird part about building a product right now and a company right now, because so much of what we write we plan to throw out later. Yes. And this is a very weird way to build a company. So Google's chain of thought paper, which preceded 01 and doing reinforcement learning on chains of thought, was out roughly when we started the company. It was an earlier paper and effectively provided sort of a substantive basis of why asking a model to explain its reasoning step by step produced more robustness. So we use chain of thought all the time. And it was like a methodology we used.

46:27Bret Taylor:and then you know open ai uh very innovatively like came up with the idea of we could do reinforcement learning on those chains of thought which is where oh one came from and then most labs are doing that now so we we'd throw out things like all the time you do it and you're like okay the model just does this for us now we work with a lot of financial services firms we work with one bank that has a large you know hong kong business and they speak cantonese you know and like okay well, we need really good Cantonese voice support. And it turns out that that's really hard, and there's not an obvious model that does that.

47:02Bret Taylor:So we spend all this time evaluating all these models. What certainty would you ascribe to every voice model supporting Cantonese well in three years? 100%, 99 %? Pretty close, yeah. So we did all this work. And in fact, we, I think, have the best Cantonese support on the market. Great for us, and it's a huge selling point. And it's a technology that will certainly be commodity commoditized in three years so a lot of what we think about i think is going from essentially technology innovation now i think a large part of why we work with the largest companies in the world is because our technology works yeah in three years the same clients will work with us because we have the best product and i think and if you look at the early marketing for like early software as a service companies, they'll explain why having multiple tenants in the same database is safe.

47:54Bret Taylor:And that was a huge part of their marketing. Nowadays, if you came and you marketed your product that way, people are like, what are you talking about? Like, I don't care what database Stripe uses, you know? I think we're just at this period where the technology is so immature. It's a very technology forward conversation just because it's like people are figuring it out. just like when Netscape's business was like monetized through a web server back 100 years ago. And it will evolve from being a technology forward conversation to a product forward conversation. So the interesting part about building an applied AI company is you can't have the luxury of waiting for all the models to catch up with your aspirations.

48:33Bret Taylor:But you know they will. But you know they will. Yes. So you have to have the best technology and have to be comfortable with throwing it out. And so it's a real momentum and pace of innovation game, rather than thinking of this as like precious intellectual property, if that makes any sense.

48:49John:It absolutely does. But isn't this organizationally hard where if I'm the head of Cantonese language as Sierra, my incentive, and then not disingenuously so, I'll notice all the corner cases where the models aren't that good in Cantonese. And obviously we saw this in prior tech waves, right? Where the cloud adoption laggards were companies that had their own on-prem stuff, and they had a million reasons, half real, half fake, as to why cloud did not suit their business purposes. But how do you avoid getting stuck in this mode of thinking where like, oh, well, their chain of thought doesn't do what we need, is like the classic thing you would hear from someone within the organization.

49:36Bret Taylor:It's a huge shift. I mean, going back to the first thing we were talking about, it's hard for me to not care about the elegance of the source code, which I think is an impediment to my fully realizing being a software engineer in this new world. I think teams that start to treat the code that they wrote as precious, that has been obviated by a general purpose AI model, will fundamentally fall behind.

50:04John:Public markets deem the software industry 20, 30 % less valuable than they did maybe three months back. A day ago. Yeah, exactly. Very recently. And the two sides of the debate are, one, the valuations were based on what the businesses will do in 2030 or 2035, like far in the future. And just there's much more uncertainty there. And so this is deserved. And the counter argument is that it's still not the case that agentic software production is really going to build you a workday. Indeed, Anthropic just installed workday, very famously. Where do you net out on is this a rational response or not?

50:50Bret Taylor:I think it's rational, but I think it's a bit overblown at the same time. So I think it's rational just in the sense that there's probably been there hasn't been more uncertainty in this market ever. Yes. And so unless you have a strong thesis about an individual company, my guess is like, will these companies be less valuable 10 years from now than now? I think the answer is probably yes. Will that be true for every individual company? I don't think that's true. And so if you're just thinking about, you know, a portfolio of investments, I think it's sort of an indictment of the sector more than it is an indictment of an individual company.

51:28Bret Taylor:I don't know if the value of these platforms was who could Vibe code in a weekend ever. Not that we knew what Vibe coding was. My point is, you know, everyone who's ever built a software as a service application has had a Hacker News comment of I could have coded this in a weekend. Like every single one. Famously Dropbox. I'm sure you have as well. every single product I've ever made. It just happens. It's like a right of, in fact, if no one said that on your product, like I'm sorry. It's not relevant. Yeah, you're not relevant. It's not interesting. And obviously most of those comments were incorrect.

51:58Bret Taylor:But if you think about, you know, all the work you've done in compliance or the relationships you have with large financial services institutions, the work you do on fraud, the things under the surface that aren't, you know, the forms and fields in the web browser are actually incredibly valuable. If you think about a large software company, You know, they'll have thousands of quota carrying account executives that represent sales capacity, which is basically a channel. And distribution turns out to be a very important part of software. There's social proof. There's the old saying, no one gets fired for buying IBM, which few people say right now.

52:35Bret Taylor:Though IBM is actually like doing really well under Arvin. You want to be maybe the first health care insurance company to adopt something. There's another health care insurer who says, I want to be the fifth. I want other people to prove it. There's all these network effects around these businesses and scale and moats and sort of Silicon Valley speak around them. I think the big risk is where is value in the software industry and years from now? One risk is that more people will build than they do now versus buy because the marginal cost of writing software goes down. I think that'll be true for some software, particularly developer platforms and things like that that are already being consumed and purchased by other engineers.

53:23Bret Taylor:Little libraries or, you know.

53:25John:Things that already were part of the build versus buy calculus, it shifts the balance of power. Absolutely. Yep.

53:30Bret Taylor:The other part of it is systems of record. So I think these systems of record have always been sort of the gravitational center of their relative solar systems. And it roughly breaks down by department. So ERP systems are associated with the finance department. And SAP and Oracle and Workday have ERP systems. And you have Adobe in the marketing department. And they had Salesforce in the sales department. And you had ServiceNow in the IT department. And everything sort of rotated around them. And why? Well, first, their database was sort of truly the system of records. So every application that wanted to interact with the data in that had to, you essentially collect taxes from your ecosystem.

54:16Bret Taylor:And then similarly allowed each of those systems of record company to essentially have revenue expansion opportunities to go to adjacent areas where they're all sold to the same buyer and all of that. But the thing that's really interesting is AI agents are actually performing valuable labor. Is the database and the system of record, does that continue to be the gravitational center of each of those workflows? So I'll just take marketing as an example. The database of, you know, your customers that you use to drive, you know, sending out an email blast on Black Friday has some value. but if you had an ai agent that drove like way higher like more leads for your sales team from that marketing blast you probably that's worth more to you than the the system of record itself similarly if you imagine uh you know uh i'll just take like a crm system and you think about the ai agent that's carving your territories if no one ever logs in to actually do it manually all of those things have a lot of value and a lot more value than, you know, relatively speaking, than they did because they're actually performing the action.

55:32Bret Taylor:And so the real question to me is, does it upend this, I would say, something that's been true for 30 years, which is all the value is in these systems of record. And the way I think about it is agents are, to some degree, a system of record of a process of generating a lead or auditing your financials or reviewing a contract or whatever it might be. And I don't think we've ever had a piece of software like that. And will those encoded, well-optimized processes start to have more value than the databases? I don't know that's the case. For example, if your ERP system is your company's ledger, that'll have a lot of value.

56:09Bret Taylor:But I wonder for all these others, and my theory is the closer you get to literally the database is the value, i.e. a ledger, the more durable it is. the closer you get to be in a system of engagement, the less terrible it is.

56:23John:That's very interesting. Yeah. And it kind of gets back to the point you were making about the company that was looking to standardize and not have three different DRPs and stuff like that. And you're like, oh, why? Just try not doing that. And I think maybe the consumer example of this is I think people have probably had the experience of you paste data into an LLM to do something with it. And you know, like the formatting is all messed up and like, you know, the tabs and spaces don't come through and everything like that. So it doesn't matter. LLM doesn't care. You can just like pay through whatever and it'll work with it.

56:57John:And so this idea that, as you say, if like the system of record is important because it's your general ledger and it matters to the auditors, that's one thing. But if it was a system of record in this kind of all your data in one place way, and because it was easier to build incremental software atop it, maybe that advantage is going away because the agents are fine plucking data from 10 different places.

57:22Bret Taylor:That's roughly my view. But the bull case, I always mix up bear and bull. I need to spend more time on Wall Street. The bull case, though, is I think all these companies sort of have a right to win. They're all big. They still have sales capacity. They have all these advantages. But it's a race. How fast will smaller companies build differentiated, scaled businesses before the incumbents grow into this new world? But for a wide variety of well-documented reasons, like disrupting their own business model, it's harder. But I think your ask is, is it irrational? I don't think it's irrational. I think there's just more uncertainty now than there's ever been.

58:04Bret Taylor:And I think markets are telling you there's a lot of uncertainty. And that's why you see people recede from the whole category, basically.

58:12John:I feel like there's also a totally separate thing playing out here where for a long time, certain companies were criticized for not taking profitability that seriously. And at some level, there's just a return to normal valuation levels on like a fully loaded stock-based comp baked in and everything basis. That's kind of independent of the AI thesis, but maybe just some return to, again, on a fully loaded gap basis, more normal valuations?

58:44Bret Taylor:Well, essentially, if you look at a traditional software as a service company, the way most people model it is you have annual recurring revenue, which is basically an annuity, and it should throw off that much cash every year. Then you have attrition, which is subtracting from the annuity, and then you have net new ARR, which is adding to the annuity. Your salespeople sell software to add to the ARR. you typically have like a account management team or customer success team to keep churned down. And you grow that annuity and you grow your headcount often just a little bit ahead of that annuity because you need to grow a new business.

59:22Bret Taylor:And if that annuity is not an annuity - Yeah.

59:26John:Then that math really changes.

59:28Bret Taylor:It really changes. And so because the whole idea of software as a service is you can just slow down hiring and you become very profitable because the annuity starts starting off cash. That's been the thesis of every private equity firm who acquires slow growth software service companies. If you don't assume that that revenue is going to be there two years or three years from now, your discounted cash flow analysis looks pretty different. And I don't think it's actually quite so dire in the timeframes that people think. But again, if you're asking like markets are, there's all those great quotes about Wayne and I don't know.

1:00:05Bret Taylor:Voting Wayne machines.

1:00:06John:Voting Wayne machines.

1:00:07Bret Taylor:Yeah, I get it. There's probably more safer sectors to invest in. But I don't think it's an indictment of individual companies. That's my point. I actually think when we first met, I doubt either of us had an extremely positive view of the future of Microsoft. At the time, it was like it felt like a previous generation company. Now you look at Azure, their open-air relationship, all these things. Like, what an impressive turnaround. So, you know, I think any one of these companies could do it. I think it's just, but it's more of an indictment of the market.

1:00:39John:Yes, yes. I have a lot more questions. Would you like to know the goodness? Sure.

1:00:46John:Brett has been through a few platform shifts. And one thing he's been pretty consistent about is being mindful of the external forces that are shaping the ecosystem you're in. He talks a lot about building with the broader wave of AI agents in mind. Stripe Sessions is our way of helping builders see that wave up close. What's changing in the internet economy, what's actually working in production, and what the next era of software looks like when agents are running real commerce workflows. It's not the usual conference fluff, it's insights into what the fastest moving companies are actually after.

1:01:13John:So if you want to experience the next chapter of the internet economy firsthand, join us at Stripe Sessions this April. Use the code CheekyPound for 50 % off a conference pass at sessions.stripe.com.

1:01:28John:You talked about business models. Are you guys usage-based? How are you innovating on the business model front, or are you?

1:01:35Bret Taylor:We are trying to. So we do outcomes-based pricing. So for a customer service context, that means if the AI agent resolves the case, no human intervention, there's a pre-negotiated rate for that. And if we do have to ask it to a person that's free for sales, it would be a sales commission. And wherever possible, there's a way to align our interests with our clients. We choose it. And I'm a huge believer in this. I think the analogy of going from impression-based ads to CPC ads is apt. I don't think any ad platform thinks like, man, think of all the impressions we're giving away for free. Because when you charge for something closer to a business value, it's actually more valuable.

1:02:17Bret Taylor:It's more efficient. It's a lot more efficient. And I think the idea, if an agent's outcome is measurable, it's a really compelling way to, both for clients, obviously, because it's aligned with their business, but it's also quite disruptive because most, I'll say, legacy software companies are not necessarily equipped to do it for a variety of reasons I'm happy to go into, but it's just a very disruptive model.

1:02:42John:Yeah, there's kind of a few lenses you can have on it. One is that you get more alignment, like usage-based is more aligned than other ways of charging. And as you say, it's more efficient because you're incentivized to drive the right outcomes. People also make the analogies to, you know, it's almost like more correct for the labor substitution dynamics that you get. Or just like because you have real inference costs, you're going to have to do a usage-based model. I mean, like, do those factor in at all? Or this is just, it would not be possible almost to a fixed price contract because?

1:03:15Bret Taylor:I would actually argue outcomes-based is pretty different than usage-based.

1:03:19John:Okay.

1:03:19Bret Taylor:You know, just because think of it this way. If you have an AI agent that is making sales for Stripe to small businesses, and, you know, I told you I will sell, you know, one-tenth the number of new, you know, Stripe GMV, however you'd value that. but I'll use, you know, one hundredth of the tokens, you probably wouldn't care. Like you care about, you know, the value to your top line of your business. I would argue there's not a strong correlation between token usage or utilization and value. There may be, but there's not always. You know, there was that infamous, I think it was called folklore, but it's this website where that Apple engineer used to put just all this Apple folklore.

1:04:04John:Folklore.org, yeah.

1:04:05Bret Taylor:Folklore.org, yeah, I love it. It's like if you're an engineer, it's a fun site to go to. But there was a story about some new bozo manager asking for lines of code every day. One of the engineers wrote a negative number as a way of saying like F you to the man because he refactored a code base or whatever. I think that is the essence of why tokens are not correlated with value. They may be, but the idea that they definitely are, I don't think stands to reason. And so I think usage-based is like charging for storage or something. outcomes based is what business outcome is this agent designed to produce and did it produce it effectively and that is really aligning because it creates like this whole vertical alignment so as a company reducing your token utilization for the same outcomes is your problem not your customers and that's a great incentive to just drive more efficiencies over time it means that to grow your relationship with the client you actually have to make your product better and not just theoretically better to steak dinner, you know, like better, better.

1:05:08John:How do you have usage based, or sorry, outcome based when you move beyond customer service where there's a clear, was this resolved or not, to product usage where people were shopping and yeah, they didn't like buy a house there, but like they mostly don't buy a house on most website visits, you know, but it was a successful visit.

1:05:30Bret Taylor:So it's the right question. And there's not a great way to do it for every type of agent right now. And so you can all sort of like fall back to usage-based, which is fine. But in that over time, it's like, wouldn't it be interesting? I think AI agents should have memory. I think AI agents should drive relationships, not conversations. And it would be really interesting to say, could we make an AI agent that actually drives home ownership over time? I think that's actually, it's hard, but it's not.

1:06:00John:Do you have a territory kind of?

1:06:02Bret Taylor:I think so. I mean, even because it's hard today and, you know, we're a pragmatic company, you know, I think it's sort of the right thing to ask, though, because that's fundamentally the value the software is designed to produce. And so, you know, I think actually it's a really sort of values aligning thing. It also, though, changes the dynamics of a software company's relationship to its partners, to its clients. Because if you go back ancient history four years ago, you know, there was a really stark separation between software and implementation and usage. You know, it was the client's accountability to use the product well.

1:06:41Bret Taylor:You know, it was either your IT team or a systems integrator's responsibility to implement the software. and the job of the software company was just to make it and throw it over the wall. Obviously, it's not exactly that, but that was kind of the mark we were in. And everyone had good intentions, but what's the same success as a thousand fathers, failures, and orphan? When the software didn't go well, everyone was blaming everyone else. The client was like, I'm using it just fine. It was implemented poorly. The person to implement it was like, no, the platform's broken. The platform people would say, and it was like, everyone's pointing at everyone else.

1:07:17Bret Taylor:what's nice about outcomes-based, whether or not the client sets it up, you become more accountable to help them be successful because until they do, they can't use it. If there is some long sort of last mile of implementation, it creates a strong incentive for the software company to have skin in the game to just help you navigate that last mile. I think so many of the problems in the software industry are due to that lack of accountability. If you talk to any company's ever implemented an ERP system. It's like a multi-year process.

1:07:50John:It's invading Russia. Yeah.

1:07:52Bret Taylor:And you don't even remember why you're doing it midway through. You've gone through two CFOs and three CIOs by the time it's done. And we're like, okay with that. That's just the way software works. And so my view is just like, I think, you know, AdWords sort of changed the advertising industry on the internet, you know, just drove it. And I think you can even pay for mobile app install now directly and truly pay for outcomes, I think it's a really positive step forward. So it's not going to be possible for everything. You have to have pragmatism, but I think it's the right way to actually have a partnership.

1:08:25Bret Taylor:You should share in the outcomes.

1:08:26John:You want to wire the company to be thinking in this outcome-based way. And like in your main customer service stuff, you can do that in other ways. You might not be able to yet, but you want people to be spring-loaded to be thinking that way.

1:08:35Bret Taylor:That's right. And if the whole company is incentivized towards outcomes, we're like a way better partner to work with because of it. I mean, we find this at Stripe where, again, we have outcome-based pricing. You've always had outcomes. Exactly.

1:08:48John:Yeah, it's transactional. But we find there's a lot of uplift we can get on just getting people more revenue and finding ways to, you know, we're sometimes hammering customers where it's like, you should be accepting local payment methods for internationalization, or like, you're crazy not to be turning on this feature. But we really feel it because we have the same incentive as the customer. It's like, this will be revenue maximizing for both of us. I'm going to ask a very AGI-brained question. I just can't resist.

1:09:13Bret Taylor:I'm glad we're in our second goodness now. Exactly, yeah, now that we get to it,

1:09:16John:which is you described building stuff that you know you're going to throw away because the model capabilities will get there, and you're like, occasionally, they are developing capabilities that you developed yourself. Isn't Sierra itself kind of shortage?

1:09:32Bret Taylor:Sorry, I said I couldn't resist. No, it's the right question. You know, the short answer is I don't know. I mean, the fog of war in the software industry is pretty thick right now. I really believe in the applied AI market, though. I think most companies don't want to buy models or buy software. They want to buy solutions to their problem. And if you just go back to the cloud industry, why doesn't Amazon and Microsoft do everything for everyone? There's not really like a sort of by somewhat similar logic like why should any software as a service company exist when you have bigger scale, all this technology.

1:10:15Bret Taylor:In theory, they could just develop all the software. And actually, many of them have tried. There's actually competitors to Salesforce and almost all of the above. I think there's so much nuance in how these companies align themselves with different departments at these companies, solve their very unique problems in very specific ways that is a mix of product, not technology, but product, go-to-market, it's an ecosystem around it. And I think a lot of that still exists because I'm not sure coding the software was necessarily the hard part. And then similarly, I actually think, especially in enterprise software, how you engage with your clients really matters.

1:10:54Bret Taylor:And, you know, I think it turns out that, you know, GPT-5 and, you know, Claude, whatever version it's on right now, or Opus, excuse me, is sold to a different buyer than like the CFO or the chief customer officer or the chief digital officer. And that seems small, but it's actually big. And so I think you tend to see software companies orient around individual buyers within companies. You tend to see consolidation around departments and around buyers. It's possible that you can go beyond those lines, but it hasn't happened traditionally. And I think the reason for it is most business users want actual solutions to their problems, and they want a company that serves their unique problems in a very specific and bespoke way.

1:11:41Bret Taylor:So I actually am extremely bullish on Applied AI. I actually think we could accelerate. I'll make one statement, which is I think if we paused model development, we'd still have trillions of dollars of economic value. I totally agree. That have yet to be realized. And I think if we had a mature applied AI market where the CFO could go buy that agent to onboard new supply chain vendors that just worked, we could actually accelerate that trillions of dollars of economic value. So I think not only am I somewhat skeptical that there will only be like two companies in the world, I actually think one of the main things impeding adoption of AI is the lack of existence of all those other companies.

1:12:22Bret Taylor:And so many of the startups, particularly around here in San Francisco, are basically doing relatively rote kind of tools around the AI, rather than actually building agents for business processes that are boring but important and valuable. So I'm really bullish on it.

1:12:38John:Yeah, yeah. Yeah. And I guess you help companies ensure that they can always have access to the latest models, which sounds like a minor thing, but like the leading model is always changing. And so that's not a trivial.

1:12:49Bret Taylor:I agree. And I don't know, like, I'm not sure how much of a long-term value is. I think it is. You know, I think your customer...

1:12:55John:Up to this point, the race is led by a matter of months, right?

1:13:00Bret Taylor:Well, every single month, there's a new frontier model and your customer experience doesn't change that frequently. So you're absolutely right. But I also think there's just a big product. Our clients use it to optimize their sales. And that is a product, not a technology. And it's very particular to the workflows of people building customer experience teams, building sales teams. And that's really what we're focused on. And I think those departments deserve purpose-built software. And I think there will be enduring value there. But it's interesting. It's the right question to ask. I don't think we've ever lived in a world where production of software was easy.

1:13:36Bret Taylor:And, you know, software engineering was the most scarce asset in a company, and now it's the most plentiful. And I don't think we've ever lived in that world.

1:13:44John:Yes, yes. Well, that kind of gets to one of the biggest conundrums in Silicon Valley right now is what will the shape of AI productivity be? and I think there's a strong sense that the AI has gotten really good and it should change the composition of companies and should change the hiring plans somewhat and you're seeing this in some corners you know Block announced their 45 percent 50 percent AI layoff yesterday and you some companies are not growing as quickly. At the same time, in coding, you see a lot of AI benefits. You can kind of argue that either way, right? You can say, engineers have gotten much more productive, therefore we should hire fewer engineers.

1:14:40John:Or you could say, engineers have gotten much more productive, the ROI on a single engineer is way higher, like we now have super engineers that we can hire, therefore we should hire way more of them, because there isn't a a fixed amount of stuff for Stripe or any other company to do. And then the AI productivity story in other roles is just a bit less clear because as we've discussed, AI is kind of uniquely well-suited to coding. And so what do you make of just, how does the AI productivity show up? I feel like every company in Silicon Valley is trying to figure this out right now.

1:15:11Bret Taylor:Well, first I think I'll go back to my, why I believe in applied AI. I think the atomic unit of productivity in AI is a process, not a person. I don't think AI, I don't know if you have an assistant, but if you do, he or she might help you prepare for a podcast, might help you prepare for a meeting. He or she might also get you a cup of coffee. AI will be really good at the first two, but quite poor at the last one. So no matter of AGI, short of robotics, will get you a cup of coffee. So I think it's wrong to think about AI as like sort of replacing people in addition to being inhumane. It's just sort of nonsensical because AI sort of operates in the world of digital technologies.

1:15:57Bret Taylor:And I think if you go to like an example of even a mundane process in your business, like onboarding a new supplier, think about all the departments and people involved in that. There's a legal department to do a contract. There's some finance department procurement to negotiate the relationship. You probably have IT that's involved to sort of onboard them into your core systems. And then there's usually a business that's sort of sponsoring it. Fairly mundane happens all the time. Let's just say you tracked what is the median amount of time it takes to onboard a new supplier, and it was 17 days, just for argument's sake.

1:16:35Bret Taylor:Like, I bet you could say as a CEO of a company, I want to use AI to optimize that process and make it 17 hours or, you know, one day. And you could go through and if you had a product manager on that and optimize every part of it, I bet you could achieve that. But the hard part isn't like a person's job. It's actually all the systems and people in between it. And so I think part of the reason why I think it's been slow to get the productivity enhancement is we sort of ship our org charts as companies naturally. That's the natural state. There's not usually a person responsible for that process.

1:17:10Bret Taylor:There's the legal team responsible for the contract. There's a procurement team. So I think actually we will end up reimagining our companies with the benefit of AI. Will we actually think of our companies as a collection of processes, have people responsible them with KPIs who can apply AI? And I think I bring it up just because that's my theory of the world. I might be wrong. I might be right. But I'm not sure our companies are set up to essentially absorb the benefits of AI efficiently right now. And we need to do that to really do so. But the bigger point, I think, is that there's the paradox of, well, you want more software engineers.

1:17:49Bret Taylor:But on top of that, most of the world isn't just digital technology. And so I think a lot of the people in sort of the AGI community have only ever worked at like a research lab or a software company. you look around like wow yeah i was gonna do all of this and as they walk by the flower shop and get their coffee at the coffee shop and you think about like your the local flower shop like if you took all the ai in the world and gave it to that you gave it super intelligence like how much would impact the flower shop's operations like maybe a little i mean i'm sure it would help yeah don't get me wrong but someone's still you know clipping the ends of the you know stems of the flowers is arranging the bouquets and, you know, thanking you on your way out the door and congratulating you for your daughter's wedding or whatever it is.

1:18:33Bret Taylor:And so I think if you think about, you know, what parts of the economy can absorb intelligence really efficiently, it's certainly software. And we're seeing that already. It's finance seems particularly meaningful here because so much of finance today is just digital information. You know, we've sort of everything's in digital systems now, not even just crypto. I mean, just everything's in digital ledgers everywhere. It It still doesn't touch a wet lab. It still can't do a clinical trial. You still need to get a crate from this country to that country on a ship. So as a consequence, I think I'm not sure we'll see the productivity enhancement we see in software in every sector as quickly.

1:19:13Bret Taylor:And then on top of that, I think companies need to stop just giving like co-pilot to every employee and be like, we're AI now. and start to think about from first principles, what are the parts of your business that have a lot of digital workflows? Where can AI have a real big impact? And how do you actually set up your company to actually have someone accountable to drive that? And that feels like a real big change management opportunity that most companies haven't done.

1:19:38John:Well, yeah, just to push on that. So software engineering, I think we clearly are seeing a lot of AI productivity gains and software engineers have always loved tools and the latest tools and are just kind of headlong diving into it. then you have stuff like you're seeing the flower shop where uh just and stuff that requires really good robotics that we're far away from uh that will take a while what i'm talking about is like

1:20:01Bret Taylor:the there's like a by the way i might prefer a flower shop with the florist totally yeah yeah just to say it i'm not sure it solves i'm not sure it solves the problem i have with my flower shop absolutely i might be wrong i might be unique in that yeah but i that's but i think a lot of the

1:20:17John:a lot of the economy is actually white-collar knowledge work, not coding. Think of finance departments, legal departments, things like that, where you should be able to see a lot of AI uplift and a lot of AI productivity improvements. It just feels like a current course in speed, we're not on track to get those productivity improvements.

1:20:43Bret Taylor:Well, I'm not sure I'm right, But I would argue thinking about it by department rather than by processes where it's off.

1:20:50John:We can talk about the processes as well.

1:20:51Bret Taylor:Hear me out, though, on this. Because if you said, I want to make the legal department more productive, so I want to make it easier to do red lines, and you optimize that. But why is the contract there? What is it for? You might, if you're, for example, onboarding a supply chain vendor and you have hundreds of them, you might actually say actually making an abstract technology for your legal department to redline contracts more efficient is actually a harder, more general problem than for your supply chain vendors, because you might actually have very rigid rules around your supply chain. Let's say you're a CPG company.

1:21:31Bret Taylor:And you might actually have very specific saying like, look, if you want to work with us, here's our core legal terms. Here's the axes of independence. And if you want to make an AI agent to automate that contract, that's actually a much more narrow problem domain that doesn't require general purpose redlining technology. In fact, if you sort of reduce it, you could say, well, there are like 10 % of our suppliers where we let them negotiate their contract, but only for this spend. Let's have them go through our legal department. The rest, let's do it all with AI. And my point on it is, if you look at it through the lens of like an end-to-end business process, you can turn science into engineering.

1:22:09Bret Taylor:And I think solving legal through AI, that's a science problem. And this is my point, though, which is I think people are going through department by department. Similarly, there's not like a person accountable for that end-to-end process. And the more you can narrow the domain that you're solving with AI, the more you can build a harness or a scaffolding with existing technology to actually fully automate it. And my hypothesis is most companies just aren't set up that way. That's just not how we're organized. And as a consequence, we're all like optimizing our site. We're all just installing Copilot and Copilot's great, by the way.

1:22:45Bret Taylor:Didn't mean to insult it, but it's not actually like, yeah.

1:22:49John:Yeah. And to be clear, that's the kind of thing we're doing where, and obviously good companies did this before AI continuous process improvement. And I feel like that is the best thing to do. And I think what you're saying is there's no such thing as an AI lawyer. Instead, there's improving your commercial contracting. That is a thing that you can tend to do.

1:23:10Bret Taylor:And even more narrowly, pick one domain of commercial contracting and solve that. And I actually think those are truly solvable. And I think the companies that really think about their business that way, I think they can see the value. And again, I'll go back to the immaturity of the applied AI market is probably one of the bigger barriers right now. And my hope is that as the applied AI market matures over the next few years, we'll see kind of a step change in productivity. Yeah, there is a canonical way to build a Silicon Valley company.

1:23:44John:You have engineering and product and design. You have this number of ratios of engineers to product managers and engineering managers. And then you've got a market organization and you have these pipeline coverage ratios and you have the product marketers and all this kind of stuff. But I find it interesting how similar so many Silicon Valley companies are to each other because they've all learned from each other, right? There's like a shared recipe and a shared playbook as to how to build a company. And it gets tweaked, but ultimately, I think it's pretty good IP. Like, certainly, companies are much better off with it than without.

1:24:17John:How is that canonical template for building a company different post-AI than before?

1:24:24Bret Taylor:Yeah, that's a really interesting question. One is, I've always believed in the primacy of tech leads over engineering managers. Both Google and Facebook, where I spent some of my early career, both did this well, where in a product review, you weren't just talking to a manager. You were talking to the tech lead and PM who were product manager who were building the product. Whereas if you went to companies that produced worse software, I'd notice you sort of move up the chain of the command like the military. Yes. I think that we will end up with individual tech leads who, because of the existence of AI agents, will become even more important, where if you are a, I'll say, product engineer, I'm trying to find the right word for it, we might invent one, who has taste, but didn't necessarily know CSS, who has infrastructure ability, meaning that you understand the basics of distributed systems and debugging, and you understand your customer very deeply, with the presence of Codex, you can produce amazing results.

1:25:44Bret Taylor:Those people are truly worth 1 ,000x other people because it's relatively easy to find someone who's a great infrastructure engineer. Not easy, easy, but like relatively. Finding someone with good taste, that's relatively easy. Find someone who also understands your customers extremely well, like the nuances of the problem they're solving, those people who can combine that will, I think, end up being able to actually produce products, like capital P, valuable products, with relative autonomy. And I wonder if it will change our view on generalists broadly. I've always sort of identified myself as a generalist just because I've been both a software engineer in a suit, basically, and I've gone to kind of back and forth in that world.

1:26:33Bret Taylor:and as companies grow you tend towards more specialization you know just because the person who's sort of the jack or jill of all trades ends up sort of not fitting in you know like there's not really a place for them because okay well you're not really the deepest engineer you're not really the best designer you're not really a product manager if you've been at the company for a while we'll give you an honorary something to do and you have to lead through influence and could that person actually endure as one of the most valuable people in these companies? And I think, I don't know whether it's naive optimism or true, but I actually think those people who often exist in early stage startups are often the people who get sidelined, but actually in a way that actually really harms the company.

1:27:19Bret Taylor:And I'm hopeful that in a world of AI agents, those generalists who, again, I think the most important part is understanding the customer need with agency, no pun intended, and empowerment can end up more powerful in the Silicon Valley company.

1:27:36John:I've noticed the exact same thing, it's right. The exact same thing, which is high agency, really caring about customers, just really caring generally, high work ethic people who maybe weren't the best engineers previously or now, those people are massively ascendant as far as I can tell because they suddenly got the exoskeleton. And they always have the ideas as to what we should be doing and this is the better way to serve the customers and everything like that. But now they have the way to make all their schemes real. I've really noticed that at Stripe.

1:28:16Bret Taylor:Well, it's interesting you talked about work ethic. It's addictive right now because you can do so much with the technology. everyone I know who's really used it works harder because it's like wow yes I could do so much you know like you think like you're about to go to bed you're like should I get an AI agent to do something like am I wasting my the next uh you know eight hours of my life and uh I you know that might be a novelty that wears off but I think it's really exciting so I'm hopeful on the product engineering design side, you end up with these hyper high agency people who really deeply care.

1:28:56Bret Taylor:I really like the way you said it actually. It's right. It's not just customer problems. It's like care, period.

1:29:00John:Just care

1:29:01Bret Taylor:can end up more empowered. And I'm curious what that means for organizational structures. You know, it's... I think we have a new job role we need to invent. It's like, what role are these people in?

1:29:11John:Hyper generalists. Yeah, like kind of product managers, but sometimes maybe without a product, like minister without a portfolio, they're just doing stuff but now they can do much more.

1:29:20Bret Taylor:Yeah, and it's almost like product designer, product manager, engineer. That's why I said product engineer, but that means something different. But it's interesting because we've talked about this. You know, you end up where the grass is always greener with orange structure. So you go functional organization. Okay, we're going to have engineering and product design. Let's go to business units. They're like, wow, that led to silos and war infections. We're one reorgulation. Yeah, you sway back again. You know, that's welcome to. Just one more reorgulation. Yeah, exactly. I'm a middle manager now.

1:29:47Bret Taylor:And I think that it is interesting if these people become extremely important, what does it mean to organize around them? And I think it does feel like something that will end up flatter just because of the amount of impact an individual can have. And so that feels really exciting to me. But I don't really, it sort of feels like a blurry picture right now. Enhance. I agree. It's very blurry.

1:30:14John:It's so interesting. You were on the Twitter board during the super interesting takeover battle with Elon Musk. What are your reflections on that experience a few years later?

1:30:32Bret Taylor:It was really interesting to sort of be in the public spotlight. I hadn't really experienced that in my career before. I joke like no one really cares about enterprise software. I worked for Salesforce for six and a half years. Sorry, that's a joke. And I worked for Salesforce for six and a half years, and I don't think my mom knows what Salesforce does. And so to have something that was not really just like a business issue or a technology issue, but like sort of in the mainstream, I realized I didn't love that very much. You prefer enterprise software? Exactly. I'm like a builder. I like to build things and have people use them.

1:31:15Bret Taylor:I know it sounds sort of funny and reductive. That's what gives me joy. So the one thing I realized is the conflict of it all, however it turned out, like victory, defeat, whatever it was,

1:31:27John:it just wasn't something that filled my bucket very much. What do you make of the fact that in all these kind of headcount debates, Elon is now running Twitter with 80, 85 % fewer people? I think Nikita Beard tweeted recently that all of Eng products and design at Twitter is 50 people. And, you know, maybe it's been a little flaky in pockets or just at times, but mostly the service works and they have shipped new features. And I think those two statements are undeniable. But just what's your takeaway from that?

1:32:03Bret Taylor:I don't know. I haven't followed as much as sort of the like, I didn't see that tweet as an example. Do you call it tweet still?

1:32:10John:Sorry, I'm a little fashioned.

1:32:13Bret Taylor:So I don't know about that, but I mean, it is interesting right now because obviously a lot of that predated AI. But I mean, any person who's been an individual contributor engineer knows that the size of the team does not produce like linearly greater outcomes. Everyone in the world has experienced that. So, you know, I think the, you know, the idea of can you actually give individuals with good taste more agency, no pun intended, I think it's always been sort of an enduring thing. What was Jeff Bezos, a two pizza box sort of thing?

1:32:48John:But then do large tech companies underrate this phenomenon? Like, do they pay lip service to small empowered teams and two pizza teams plus extras that maybe they should be doing more?

1:32:59Bret Taylor:I think their companies largely act somewhat rationally. I can't remember who the CEO was, but it might have been the Rippling CEO just talking about, you know, there's this idea of being like lean and agile. And then there's like, you want to capture market share and, you know, grow your product and grow your platform. and at the end of the day, you can be clever but not smart and you might be so clever to think I'm not going to have anything more than two people on these features and if you have a competitor who maybe does something a little less elegantly but wins, who cares that you are clever with your two pizza box team or two person team or one AI agent team or whatever it is.

1:33:42Bret Taylor:When someone said we're going to have an X billion dollar company with one person, I think they might have been right

1:33:49John:but it's not you could have had a 10 billion dollar company if you'd hired a bit more that's

1:33:53Bret Taylor:right and i would actually argue the more specific thing is if all of a sudden for some uh you know clever reason you want to prove you can the idea that like a competitor might have 10 people and beat you is probably more likely than even having a 10 billion dollar company and so i think at the end of the day you know when you're building a business especially one that's in hyper growth, which, you know, successful businesses in tech tend to be, if you are too clever and austere, and going back to your point about Silicon Valley cultures all being the same, there are examples of companies that really innovated in culture.

1:34:29Bret Taylor:You know, you wouldn't think of it this way, but like HP, sort of like a lot of the kind of traditional open office floor plan, you know, came from them. Facebook.

1:34:38John:Laws worked as HP.

1:34:39Bret Taylor:Oh, I didn't know that. That's And then, you know, Google offered free food to their employees, which a lot of people did. And then, you know, Facebook, you know, a lot of like the layouts of offices all looked like Facebook for a long time. But then you have other companies like we're going to innovate in HR and they spend all this time and energy on it. And in fact, the smart thing to do is just be like, we're not it's not what we do. Let's just do the same old thing as everyone else, because everything is just it's push button. I don't need to worry about it. And so I think, I do think it's the right question to ask for every technology company.

1:35:13Bret Taylor:Yeah.

1:35:15John:After being on the Twitter board during the Elon takeover, you were then on the OpenAI board when Sam got fired. Have you considered that you are the problem? You are bringing the drama.

1:35:27Bret Taylor:I came in after the drama there. Oh, right.

1:35:30John:You joined after. Oh, sorry.

1:35:31Bret Taylor:I was brought in as the mediator. I see. Yeah. Post the, okay.

1:35:35John:Yeah. Okay. Your hands are clean. You're aligning my reputation here.

1:35:38Bret Taylor:Yeah, I wasn't actually on the other side of it, but I got a phone call. Was it Saturday or Friday after? And basically my understanding was I was the person that both the existing board and Sam agreed upon to kind of help mediate the situation.

1:35:55John:What have you learned in the OpenAI board?

1:35:58Bret Taylor:A lot. I mean, certainly the most interesting part is the AI research. You know, I've never been affiliated with a true research lab before. And that's fascinating to me. It is very inspiring. I mean, it's very easy to grow, not cynical, but like, you know, you can look at, you know, OpenAI, Google Anthropic and say like, who's, you know, whose model scores better on this leaderboard to actually go in and see this company where every single researcher trying to make safe AGI and not come out of this board means inspired is impossible. Like, it's amazing. The other thing is it's the first not-for-profit board I've been affiliated with.

1:36:43Bret Taylor:And that's really interesting as well, just because... It's a different thing, yeah. Well, and I mentioned the fiduciary duty is you have a duty to the mission. And that is really clarifying and interesting as well, because when you're making decisions and you realize, you know, So you have your sole duty is to ensure that artificial general intelligence benefits humanity. That's really different. It's really interesting. I've never had a fiduciary duty to a mission before. So that's really interesting to me because I take those duties really seriously and like reflecting in a board meeting and you're making a decision.

1:37:15Bret Taylor:You think about it very differently through that context. And then the other thing was because I was brought in after that crisis, there was three people on the board on the other side of that when I agreed to temporarily be the chairman and still there. We had to grow the board essentially from scratch. And so that was really interesting too, just to think about, normally you add one board member at a time. This one was like, do you have a bulk rate?

1:37:44John:We're going to build a board, put together a team.

1:37:46Bret Taylor:So you really think about, you know, I spent time with the other two board members just really thinking about, like, what does the composition for an open AI board look like? You know, how do you represent the not-for-profit part of it? How do you represent safety? How do you represent, you know, the economic impact of AI? We're doing lots of infrastructure investments. Like, how do we find someone with, like, that specific type of financial expertise? And so that was really rewarding as well, just sort of building a board, not from scratch, but, you know, effectively from scratch.

1:38:16John:Last question, what are your AI predictions for 2026?

1:38:19Bret Taylor:I think we will have some scientific breakthroughs with AI that positively break through into the mainstream press and awareness. We've already had some interesting math proofs, but I joked with one of my friends, until I can understand what the title means, I'm not sure it's going to make...

1:38:42John:You're not excited about n-dimensional manifold space. Exactly.

1:38:46Bret Taylor:And, you know, it won't quite be like the, you know, Apollo landing, but, you know, I remember, you know, the Kasparov, you know, chess match. And I certainly, you know, things like AlphaGo were really meaningful. I, given the progress in math, I'm hopeful we have at least one moment of discovery that is inspiring. because I think a lot of the dialogue around AI right now is economic opportunities, but also what could go wrong. And I actually think one of the main things that can go right is actually discovery in science that actually can improve the human condition. So I'm really excited for it because I think it will contextualize why so many of us are excited about this technology in a way that sort of captures attention.

1:39:34Bret Taylor:So as you said, something beyond n-dimensional manifold, blah, blah, blah. And I feel not confident in that, but it certainly feels like the ingredients are there for that. I think we'll continue to see mainstream adoption of AI by both consumers and companies. That doesn't really feel like a prediction, but I think this will be really a year of adoption of agents. And we're certainly seeing that in CIRA's customer base, but I think we're going to see it more writ large. And then you already see in chat TPT growth, you know, really unprecedented levels and things like OpenClaw. You can sort of see that kind of translate over to agents and sort of the more like long running and autonomous tasks.

1:40:17Bret Taylor:So it does feel like by the time we exit this year, can that go from a niche community to something more mainstream? It feels probable to me. and then the other thing is I think most companies in Silicon Valley won't write code by hand and that might seem almost it's sort of funny that it sort of feels obvious right now you're like oh yeah of course like you're just nodding like yeah of course yeah why not but if I had said that like four months ago that would have been a bold prediction but I think that's really interesting just because that's such a fundamental state change and I say in Silicon Valley because I do think it takes a while for these tools to sort of diffuse their society.

1:40:59Bret Taylor:Silicon Valley is insular enough that I think it will here, but I'm not sure it will happen through every company in the world yet.

1:41:07John:So the year of agents across businesses and just people finally getting their kind of claw style agents and then, yeah, all code written by AI as well.

1:41:16Bret Taylor:Yeah. It's good celebrations. Great. Thank you. Thanks for having me.