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

Episode Date: March 10, 2026

Bret Taylor, co-founder of Sierra and Chair of the OpenAI board, joins John for a pint to discuss the rapid shift toward an agentic future. In this episode, Bret explains why outcome-based pricing is the future of software business models, and why he believes the atomic unit of AI productivity is a process, not a person. They cover why big companies struggle to adopt AI because they are “shipping their org charts.” Bret also discusses a new type of hyper-generalist, reflects on his experience with the OpenAI and Twitter boards, and explains why he believes we might see the end of the smartphone era.Timestamps(00:00:26) Coding(00:16:23) Sierra(00:27:14) Agentic UX(00:38:47) Building support agents(00:45:43) Co-developing with the models(00:50:08) SaaSpocalypse(01:00:50) Stripe Sessions(01:01:33) Outcome-based pricing(01:09:14) Is Sierra short AGI?(01:13:50) AI productivity(01:23:47) How to structure a tech business(01:30:25) Board drama(01:38:24) AI predictions

Brett Taylor is the ultimate Silicon Valley veteran. He was one of the creators of Google Maps. Invent the like button was co-CEOOO of Salesforce. He pushed through Elon's acquisition of Twitter. He was on the Twitter board. He's now the chairman of the Open AI board. And his day job is founder and CEO of Sierra, which is bringing AI to customer service.

He's one of the smartest people I know on the topic of how AI is changing established companies. Cheers. So, most important question, have you installed OpenClaw on your work laptop? I have not. Have you? Have you played with OpenClaught? I have played with OpenClau. I haven't bought a Mac Mini.

You can put these things in virtual sandboxes for less money. It's really interesting. I mean, it's very compelling. It's probably the first, I wouldn't have predicted the first kind of broad, I don't know if consumers exactly accurate, but maybe a hobbyist use of AI would have been this kind of semi-rogue open source project that goes through three name changes in three days. Yes. And I love it.

I love everything about the chaos of it, just because people in our circles have been talking about AI agents for consumer use and all these fancy computer-using agents. And instead, you're chatting over WhatsApp with a thing on a Mac Mini that is mildly unhinged and insecure. It's just fascinating. The whole thing that's fascinating. But isn't that, okay, the thing that seems to me is funny,

is if you look at the landscape, still in 2026, If you open a new Gemini chat or if you open a new chat chabit chat, it's basically a blank slate. There's no memory. And then, I mean, Clob, people talk about, you know, the WhatsApp and telegram integrations and things like that. But it feels to me a big part of the value is not only going to do stuff proactively, but it has memory. But the way it has memory is this like super janky. It's like the movie memento. It like writes things to a markdown file.

And it's just like writing the things to remember. And the compaction is kind of buggy. like it didn't always write down the exact right things to remember and stuff like that. But isn't that funny that you can get super polished mainstream consumer apps that have no memory at all, or this like wildly insecure three name changes project that kind of almost remembers things by scribbling notes in the margin? And like that is the state of consumer AI. I have a probably not very thoughtful, but kind of technical theory on this. So coding agents have gone through transformation.

over the past four months. Like the difference between, if we were here in October versus now our conversation about the future of software engineering would be materially different. And how often can you say that about a technology? And people always, in my circles anyway, you look at a coding engine and you extrapolate to other domains. You're like, could all digital tasks be like this?

And the answer is obviously yes over some period of time, but it's really interesting because I think sometimes, like, I think the hard part of engineering is in the details, and code repos have very specific qualities. One is all the context is in one place, in files that are largely textual, not binary. And for most broad information tasks, that's not true. You're making, like, when you were writing your annual letter, my guess is the sources of information where are in so many different systems,

data warehouses. And so it's not, like, impossible for an agent to use those things, but the idea that you can like straight line from coding agents to writing the Stripe Annual Letter, I don't totally buy. And then similarly, when the agents actually perform work on a code base, there's feedback. There's compiler errors. There's often unit tests. There's integration tests.

There's the history of every change every made in a really formal format along with code reviews. And so you can actually, it's almost designed for a robot, and you can self-reflect. maybe we as engineers are sort of like have always modeled ourselves after robots, and now we can actually fully realize that vision. So it's interesting about it is like the idea that it wrote a markdown file for memory I think is maybe more significant than a hack. I actually think to some degree... Turning your life into code, kind of.

Yeah, it's like you almost want to put all everything in a file system that sort of looks like source code, not because that's the only way these agents can work, but actually it's quite an efficient way to get a mix of context and random access memory. If you think about like a vector database, it's more random access. You have to know what to look for,

but actually that's not how real memory works. There's a mix of it. So you're loading in a markdown file. And as you said, compaction, all these things matter. But the messiness of it actually probably produces a more useful agent than a lot of the fancier things.

And I use memory in chat, and I love it. But I actually think this idea that there's a directory of just everything you've ever done is actually maybe more useful to an AI than people think. And actually, if you follow, like, over the past, you know, a couple months just this emergence of harness engineering where you're building, you know, the harness around an agent to do work,

I wonder if, in the short term, it might just be one of those idiosyncrasies of history, like mimicking a code base is actually the best way to make a general-purpose agent work. Yes. And maybe over time we'll get fancy. than that, but it's actually like a relatively efficient harness for an agent. So anyway, maybe that's why. Yeah, and it's very, you know, terminal-centric and, yeah, it's kind of backwards-compatible.

You can use grep. Yeah. You don't need to make some vector database, you know. And the AIs really know how to use all the Unix tools. And so you got a lot of lift from that. That's exactly right. I mean, you know, software engineers were notorious for making tools for ourselves first. So then we just almost like bend every other domain in digital towards that domain.

But the reason I brought up things like unit tests, integration tests, opening I did this blog post. I can't remember the engineer did the post, but on harness engineering. And one of the more interesting parts of it was documentation. So rather than just having a single agent's markdown file, they had a directory of essentially the entire product, the architecture, and they're sort of filling this out over time. And agents markdown became sort of pointers to it. my hypothesis having used codex a lot, I wonder of the output of a session where you make a change to Stripe's products should be a documentation artifact in addition to code, where the documentation artifact is actually what the product manager version of John and the code was the engineer version of John, where there's a lot in the code that is more transient. You know, you might be fine to delete that.

What was the intention? and what was the PRD, like what was the customer problem, is actually the more durable asset. And I wrote this on X and one of the funniest comments, it would be like, it would be the greatest irony if, you know, software engineering agents made all of us just write documentation the whole time just because notoriously, every good idea hates writing documentation.

Now that's our job. But I don't know, it resonated with me. How much are you AI code? Like you're a very prolific engineer in the old-fashioned hand-spun way of writing, code. And so how is that changed? Just a spoke artisital code. Yeah, pour over. That's a really fun way to use that.

I am trying to get to a world where I'm not writing code. It's hard emotionally. That makes any sense. I have a hard time not caring. I don't care about the assembly language produced by the compiler. So why should you care about the code? Why should I care about the code? I care about correctness, I care about robustness, and I think I know intellectually, I don't need to look at how the compiler unrolled this loop to verify its elegance and correctness.

Yet somehow I feel that way about code. And I'm not saying the code doesn't matter, but I've been trying to force myself to not care because I feel like I won't be like a self-actualized software engineer in the future if I'm too precious about that artifact. which used to be so central to me. Right now, writing markdown files, like maybe that's fine. It feels somewhat like a local maximum, and maybe we'll just be like, oh, of course it's marked down as hell.

You know, we work with machines. If you think about what a compiler does, there's this interesting mix of like formality and informality, and if you've used like Python versus Rust sort of different ends of the spectrum, now that you're not writing the code, I really wonder what that programming system should feel like and look like.

and I don't mind chatting with Codex, that's fine. But I also think, you know, as you imagine, like, all the tests that you care about, all the, like, it's showing you demos and mockups. And I wonder sort of what the future integrated development environment, for lack of a better term, will be in that world. So what I'm trying to do is force myself to not be emotionally attached to the code, which is very hard for me because that was my entire life before. I was, like, proud of the elegance of the code that I wrote.

But if I still care about the craftsmanship, what do I want? And I haven't quite visualized it yet. It feels to me like a very interesting time in agentic engineering, because you were talking about this domain of harness engineering and people having skills and MCP and everything like that. It's always interesting when not only is like the leading product in a category changing, we're just figuring out what the categories are, that, you know, we need things for MCP or, you know, skills or stuff like that.

And it's all very fast-moving. And that just feels to me like a very interesting time where clearly a new way of engineering is shaking out. And 2026 is clearly not the final word. Absolutely. And in fact, I'm growing more skeptical of MCP as, like, a meaningful part of the future. It's fine as a protocol. Yeah, yeah. But it's interesting.

Going back to your joke around, open clause, just writing a big markdown file. I think it works better than a bunch of MCP servers. But going back to the point of every AI agent knows how to use crap, but knows how to use all these things, I feel like this view of a multi-agent world was you have all these agents that do tasks that are fraud detection. You know, another one over here for, you know, personalization. And then you make a super agent, it does all these things.

And it looks really good on a whiteboard like most elegant-looking, but completely nonsensical architectures. And then you realize if you just, you know, imagine you anthropomorphize like the Stripe experience and you're like the checkout concierge, what information do you need to have no a priority to actually make that like a humane experience? And what ends up happening in these multi-agent systems is you stuff all the context and the subagents and the one on top has no ability to actually like not sound robotic. And then in contrast, you look at something like open clause, just a bunch of

markdown files and it's the memory kind of feels right even though it's a little bit cloogey. And similarly, if you go back to my arguments about a source control, you know, like a repo, it has so much context. So it's not like you just have the myopic view of the file you're editing. And like it really has some expansiveness. My sense is we're making true agents over time, the way we think about context and how that context is sort of like shared so that the agent that's orchestrated, actually understand sort of what's behind all these APIs and why and the history will maybe look a little bit more like OpenCla and less like MCP over time. And I think these agents need a lot more context than what MCP affords. Yes. One thing we've noticed is there's a bit

of a what's old is new again phenomenon where with this agent of commerce stuff that's happening. We actually built the APIs for this like 10 years ago as part of, do you remember that move of social shopping that was like for a while, like buying on Instagram, buying Twitter. Yeah. And there's just kind of, it didn't quite happen for a few different reasons of the time. But the concepts are very similar that you want some action at a distance. You want to be able to go kind of manipulate stuff offsite. And similarly, you know, I think Patrick has wanted for the longest time in Stripe is the ability to just SSH into your stripe account. What do you mean? It's like a very ergonomic way for developers to work is you

just be able to like log into your Stripe account and you have a community. manned there and then you can like list out all your pay or you can like tail the payments log or you he wants tail and pipe the crap yeah exactly and of course now we're building that because it's like much more relevant in the agentic world but i find somehow yeah all the agentic stuff is also bringing back i don't know if you have this experience as well it's bringing back a lot of ideas that you might have had before well it is because to some degree the elegance of unix which has sort of been the basis of why everyone wants ss and like the curl command that was sort of famous on the Stripe homepage.

For people who got it, it was remarkable because you could have all these tools that did something well that was small and useful and you could chain them together to make something great. I actually, I wonder in the future, we've talked a lot about this, you know, if you look at the canonical

software-to-service application like Stripes console, and obviously you have your, what's the consumer see, but like the configuration that a Stripe customer will log into, you would have a web app, and that's like the form, and fields and buttons and graphs.

And then you have the API. And it was typically like a REST API or GraphQL API, and you could do stuff with it. And this is how computers talk, and is how humans used it. I wonder if the web application of the future will actually be, certainly you'll want a web app, you know, for the rare human, you know,

who wants to sign in, but will you have an agent harness? And what I mean by that is something more than the APIs, but just like if you think about the harness that you provide in a codebase, the skills, the documentation, the rules. Imagine the person who's the greatest Stripe expert. Who knows how to extract the most value from the Stripe account, that's the harness. Yes. Not the API. That's just the button you click. And, you know, will that be an endpoint on Stripe.com and so that your agent knows how to, like,

just get the most value from Stripe? And I imagine you don't care of your merchants, you know, log in. What you want them to do is drive value for themselves, drive GMV, drive payments. And so I just like what I'm really excited about that because, you know, an API is great. APIs are awesome. But a harness is basically like,

here's the instruction manual for all the Unix commands that power, you know, Stripe. That's very interesting. Yes, and I think if you look at the shape of a lot of APIs that services have, and like, I think Stripe's API coverage is probably more complete than most, but ultimately it is a way to manipulate

some of the highest value business objects in the thing, whereas actually what you want is, one, all of the data to be browsable in some kind of identically accessible or textual way, and then all of the actions to be, you know, able to be taken by agents. And it turns out there's a lot of switches in the dashboard. You know, there's no API for,

and we are all as an industry collection, we discover. And it might be easier. I mean, imagine being a product manager in the future. You just need to add the switch the dashboard. You're like, yeah, it looks like a Russian submarine to switches, but who cares, right? Like, agents can handle it. And, you know, as long as the harness describes when to use that switch, you know, it's easier than UI design in some ways. And that's fascinating to me. Well, one funny point Dario made is it's not clear, well, there's a race between people getting

their stuff accessible via agents and just desktop computer use getting better. And so it's actually not clear will the approach be Stripe builds way more APIs and that's how, you know, your agents manipulate the Stripe account, or you just give your agent access to Chrome and a login. Well, so actually, I'll give a funny story here. So Sierra, my company... Sorry, we'll get to Sierra. No, no, it's fine, but there's a real funny story here. So, Sierra powers a lot of healthcare companies. So like on the healthcare payer side health insurance. Well, known for the API quality. Well, so first of all, actually pretty sophisticated engineers of these companies.

I really enjoy working with. So you end up with Cigna, Blue Cross, Blue Shield on the healthcare payers right insurance. Then you have health care providers, like Sutter Health that we work with. Then you have revenue cycle management. So like R1, a revenue cycle management basically help providers get paid by the insurance companies. And then you have a lot of other people in the middle, pharmacies, PBMs. And they all call each other. So, like, health care provider has to call a payer because a procedure happened and they have to get paid.

So we have payers with AI agents that pick up the phone. Oh, sure. And we have providers that have AI agents that pick up the phone and make phone calls. We have revenue cycle management companies that work to make outbound calls to do it. We've already had... Do they switch to the agent language? They don't. Oh.

We've done English over the publicly switched telephone network. So you have TCPIP and English over PSTN. And it goes, I mean, it sort of reinforces, I guess, Dario's point, which is you can engineer all these fancy protocols, but the rails that are already there already exist. Yes. English is spoken by all AI agents, and the publicly switch telephone network has been around for 100 years, and it all works. Which is fascinating. You have all these fancy MCP things, and we're doing, like, English over PSTN. So on one hand, I think I actually agree the principle that one of the powerful parts about AI, with its ability to do text, do audio.

and do, you know, I'll say, I'm not sure how you qualify computer use, but you can call it a form of image recognition and manipulation. Certainly that's useful because you get to the point where you don't need to like fully finish the last mile to get value. The thing I'd say though, going back to your, like talk about all the actions and just they're not all in the product, all the API doesn't exist. These visual interfaces were designed for us. So think of, I mean, you, Stripe is sort of, I think famously was one of the few interprets. software companies with good design for a long time. And the Stripe dashboard is really elegant, right? And most enterprise software, you can't say that about their dashboard.

I don't think the ideal agent harness will be that elegant because it's optimized for something else. Yes. It's optimized for the context that you need to perform complex multi-step procedures on behalf of a person. And, you know, my guess is it's just very different. And I think, you know, seeing the way you write like a harness for a software or a coding agent is just so different than the way you do I design. So I'm certain that it's great that you can, you know, click around a green screen or whatever, click around a green screen because it's an oxymoron. But type around green screen or click around sort of a legacy on-premises, you know, enterprise software system. I think these harnesses will be really good.

And I wonder if, you know, is there a world two years from now where, you know, stripes, ability to work with the agent that, you know, manages commerce for a, you know, direct consumer e-commerce company, that would be one of the ways you're evaluated, you know. And in fact, if you're, you know, for lack of a better word, harness is not compatible with the way their agents work. That's actually like that you're not compatible with them. Yeah. And I'm not sure that's right. But I, 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 pretty funny. But I don't think it's like the long-term future because there's so much value that you can provide.

Put it in other way, the agents using a sophisticated application harness can just do a lot more and do a lot more like with higher fidelity. 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, you know, the business has scaled very quickly. So one of the latest metrics that you can share because they keep changing from month to months. Yeah. So Sierra, we help companies make AI agents for their customer experience. So if you have a big phone line, these AI 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, you know, healthcare companies like Cigna. We just did a great case study with SOFi. And I'm really proud that we've raised their net promoters. score by 33 points just because it's just so delightful. It's really fun to see, you know,

sort of all these different brands across a wide range of industries get so much value from their agent. With a leader in the space, like just you talked at the metrics, we, we reached $100 million in ARR and seven quarters, 150 and eight quarters. We're, I think, around 165 now one month into our, you know, next quarter or so growing really rapidly and really proud of the momentum that we have. 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? 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 glimpses 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 or be 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.

You know, I got an offender 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 Sirius XM, if you call them on the phone, their AI agent, Harmony, which I love that name for Sirius XM,

we'll 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 literally, you have all of your, I'll say, customer experience team, or whatever you might call it at your company, they can spend all their time on one thing.

And it actually works over WhatsApp. It can work online. It can work on your website, work on your website, 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 said to a glimpse of the future, we have a few like really ambitious customers like Rocket Mortgage, great Detroit company. 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 agency. If you go to Rocket.com, you can originate a mortgage with an AI agent, and you can service that mortgage. It becomes our product usage rather than just customer service. 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. I don't mean a fun. I was a bit young, but yeah. Yeah, I would have my Nirvana shirt on. You know, we would be talking about, like, look, this is a little. like your digital front door. Or maybe we wouldn't have the pressures to say that. On the information superhighway. 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. 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. I think a company, 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 is a 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 dollars it can be much less of its more simple case and you know you have some customers who pay you millions of dollars and you'll answer the phone at the 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 like 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 customer service on the phone. But it's not because they don't like you. 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 10 or 20 cents and over time one to two cents, 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 probably better customer service. You really can. And then just think about running like a subscription business where you care as much about customer acquisition. You care a lot about churn because that's how your lifetime value equation works. Yes.

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 turn 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 they're saying, okay, what are my competitors going to do to actually take my customers away from me? And then that's where 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. They'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 was just so, so, so expensive that people like 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 like the second and third order affection could be really interesting and very hard to predict. And that's pretty exciting. I want to come back to this idea of the agent as the UI because I find it really interesting. Like we talked about this in your letter in the context of agentic commerce, who 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 a 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. It's good for yourself. I wonder just, will using websites have been actually a bit of a, like, the fax machine, you know, we used the 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 were you ever in the working world for that?

Some people still do this where they do the voice. But like companies will like blast a voicemail memo to like employees at 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 forums 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 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 see very few tablets out. Yes, tablets out. And similarly, there's more smartphones than people, but there's still about 2 billion PCs in the world. And 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 a 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, you know, significantly. And so it's almost like market share of digital interactions, which I think is a really interesting way to think about it. And certainly as you think of like Stripes 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 that 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. Totally. Speakers were just too early. Yeah. Well, it's like asked for the weather just turns out to like not the biggest market in the world.

But now, shut a timer. Shut a timer. I mean, it's amazing they've made that much money off a timer-studying speaker. And so like it is very future-proofed because it's fundamentally conversational. But maybe it's like going from, you know, punch cards, mice and keyboards, touch screens, now voice and chat and probably 3D immersive at some point. Does it just sort of add and make the other ones less important 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. 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. Yes. On the other hand, you can't talk to TikTok. Right. You know, 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. Yes. And if that's the case, can it offer an opportunity to, just sort of like unwedged some of the addictive properties of these technologies and get a lot of the benefits from it. You know, because like, at least for me, like, I think all of us are so connected. You sort of end up like, I'm going to check my email. And like, you're like, where, where have I been for the past hour? You know? And the fact that we actually have technology that affords that kind of innovation now, I think is quite interesting. So I don't know what the future is,

but I'm very excited for it. I know that's essentially cheesy, but I sort of like, we've now, like, changed the ingredients available and we have a lot more recipe. we can cook. And I think that's very exciting. Yeah. I agree with you. 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 it's, 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, what's a 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? Yeah, it turns out most of our clients have pretty different priorities. Some are very focused on cost savings, and you can automate very, very high percentages of your cases. A lot of, there's a company called Ramp that's a really impressive tech firm. We had Eric just here. Oh, that's great.

Well, they're automating 90% of their cases. They're really sophisticated, though, because they're like, you know, 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, implement 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 edit 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. Totally. To solve a hard problem. Have you tried plugging it out and plugging it again? Exactly. The other one we had one retailer whose volume, total volume, went up almost as much as they save from the AI because it was a form of that. So, you know, if you've used a chap, from three years ago, they were really annoying. Like, no one, like, three years ago, if you said, like, do you like chat bots? There was like zero people would say yes. It's so funny that there was a Silicon Valley wave of hype around chat bots.

It was even earlier than that. It was like 20-18, it was like pre-LMs, pre-transformers, yeah. And they were just like multiple choice machines or something. 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, you know, two or three X.

Yes. And the CEO was incredibly happy about it. They're like, I've just, 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, you know, with AI versus other metrics. Most of our clients are interested in the top line metrics. And so if given, 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. So that's the interesting thing going on right now because, again, going back to a minute, it was in 1994 and we're hawking websites on this podcast, I think if you 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 RB, you can't just launch AI, absorb the cost savings, pass it on to shareholders unless you have a monopoly.

Yes, yes. In most businesses, it's a consumer surplus. Exactly. So you're either going to lower prices. But I think that's why it's an overused analogy, but the ATM bank brand, 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 show of 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. 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 into 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 the world absorbs the technology we start getting into some of the second and maybe even third order effects. 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.

I'll probably say Rocket where we have a really great relationship. I think Barun is their CEO, Sean Mahohoho is their CTO, two people who, like, really are, I would say, not only just, like, curious about AI, but, like, very interested in, like, transforming the, like, home ownership experience with AI. And I don't know, like, when you, like, got your first mortgage, but it's, like, super, it's very intimidated. And it's not a modern process.

It's not a modern process. They literally call it mortgage folders for a reason. Like, it used to be a folder. And for me, it's an example of a company. like 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 it's just really interesting to see Rocket with their acquisition strategy to kind of like integrate that experience. And that's why I'm excited about. I think there's an opportunity for CEOs and like industry. like that, to have a bold vision of like what the future could be. And, you know, going back to my point, imperative, not competitive advantage, it is a competitive advantage right now. So if you imagine, like, I haven't tracked like the market share of all the big U.S.

telcos, but, you know, if you look at T-Mobile Verizon AT&T and you tracked it over the past 10 years, you end up with like surges and market share growth, the iPhone came out, you ended up with 5G. And you end up these things where, but it's my impression of the industry is you end up with these sort of like moments that drive market share and that ends up at an equilibrium.

I think that, so it's interesting about it as like 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 like actually like shuffle the deck. Is it a technology that shakes the competitive equilibrium? Yeah. Exactly right. You definitely noticed that. Yeah. So you talked about how coding is so, 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 problem is 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? Yeah, we spend a lot of time thinking about that. To some degree, one of our engineers called it almost like we're creating like 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 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. The key thing that's been a breakthrough that is probably not surprising to like the technology just listening to this, but has been a huge difference between those, like, crappy chatbots of four years ago is the reasoning capabilities. You know, I think the, you know, 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 AI agent, like, go in all three of them and just think. And they're like, well, what if there's duplicate data, what is the data conflicts?

they're like, you know, that's going to fool. And I was like, well, what does your person? What does like the person do? Like, well, they kind of think about it. And I was like, no, let's just do that. Yeah. And that's the interesting thing about these AI agents is they actually, the basic, humane, basic reasoning, not superhuman, ASI.

Yeah, yeah. 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 in that, Sonos is one of our clients. And do you have a sono speaker at your house? You'd probably have a somebody. I have had it.

If a sono speaker over 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? There's always some Wi-Fi. You know, if you wanted to make an agent to help you with your Sonos speaker, like you obviously can give it all the manuals for the speakers, all the technical specimen, 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, 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. 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 breakthrough of all that. Have you had the opposite problem where there's a customer whose problem domains mostly don't exist in the public internet? You know, it's like, we provide the drill bits used in, you know, deep-sea oil drilling. And it just like it turns out there's nothing, nothing on Reddit about that. Yeah, so 100%. 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 is 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. It's actually pretty easy because there's no temptation from the LLMs to go off script. So actually, I would say, 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. That's actually a harder problem. And so how do you force the LLMs, like mechanically, how do you force them to not answer off the top of their head, but actually look at it? 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, you know, an AI agent in Sierra and you decided to go off script, like, I got this.

Like, I don't, you know, what would end up happy is a supervisor agent would observe your reasoning, say, you know, 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. You know, here's the reasons why, you know, 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, your lay, layering reasoning on top of it.

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 evils and tests and all these other things. We can find ways to make it 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. And you started in 22, 23? We launched the company on February 13th, two years ago.

So I guess we're like our 24. Yeah, so our two-year birthday was like a couple weeks ago. What I was saying is you were saying that is, did you sort of co-evolve chain of toss 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? So yes, and it also talked about just the weird part about building a product right now 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. Yes. So Google's chain of thought paper, which preceded O-1 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 produce more robustness. So we use chain of thought all the time.

And it was like a methodology we used. And then, you know, Open AI very innovatively, like, came up with the idea of we could do reinforcement learning on those chains of thought, which is where 01 came from. And then most labs are doing that now. So we'd throw out things like all the time. You'd do it and you're like, okay, the model just does this for us now. We work with a lot of financial services for us. We work with one bank that was a large, you know, Hong Kong business and they speak Cantonese, you know. And like, okay, well, we need to.

really good Cantonese voice support. And it turns out that that's really hard and there's not an obvious model that does that. So we spend all this time evaluating all these models. What certainty would you ascribe to like every voice model supporting Cantonese while in three years? 100%, 99%. Pretty close. Yeah. So we did all this work. And in fact, you know, we, I think, of 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 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.

In three years, the same clients will work with us because we have the best product. And I think if you look at the early marketing for early software as service companies, they'll explain why having multiple tenants in the same database is safe. 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. 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 that makes any sense. It absolutely does. But isn't this organizationally hard where

If I'm the head of Cantonese, you know, language as Sierra, my incentive, and like not disingenuously so, I'll notice all the corner cases where the models aren't that good to 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? 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,

like 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. Public markets deem the software industry 20, 30 percent less valuable than they did maybe a free month back. A day ago. Yeah, exactly. Very recently.

And the two sides of the debate are one, you know, the valuations were, you know, 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? I think it's rational, but I think it's a bit over-abloat of this. 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. 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. I don't know of the value of these platforms was who could vibe code it 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

they've ever made. It just happened. 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, not relevant. Interesting. And obviously, most of those comments were incorrect. 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 or 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 a few people say right now, though IBM's actually like doing really well under Arvin.

You want to be maybe the first health care insurance coming to adopt something. There's another health care insurer who says, I want to be the fifth, you know, I want other people to prove it. There's all these network effects around these businesses and scale and, and moats and sort of Silicon Valley speak around them. I think the big risk is, you know, where is value in the software industry, you know, 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 will be true for some software, particularly developer platforms and things like that that are already being consumed in purchases by other engineers.

little libraries or, you know... Things that already were part of a build versus bi-calculus, it shifts the balance of power. Absolutely. 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 service now 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.

Every application that wanted to interact with the data in that had to, you essentially collect taxes from your ecosystem. And then similarly allowed each of those systems of record company to essentially a revenue expansion. opportunities to go to adjacent areas where they're all sold to the same buyer and all of that. 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 your customers

that you use to drive, you know, sending out an email blast of black price. has some value. But if you had an AI agent that drove way higher, like, more leads for your sales team from that marketing blast, you probably, that's worth more to you than the system of record itself. Similarly, if you imagine, you know, 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. And so the real question to me is like, does it upend this, I would say something that's been

true for 30 years, which is all the values in these systems of record. And the way I think about as 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. But I wonder for all of 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 durable it is. That's a very interesting framing, yeah. And it kind of gets back to the point you're making about the company that was looking to standardize, you know, and not have three different DRPs and stuff like, then you're like, well, 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 space it don't come through and everything like that. So it doesn't matter. L.LM doesn't care. You can just like pay through whatever and it'll work with it. 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. That's roughly my view. But the bull case, all's mix up Barren Bull, they 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. you know, 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, you know, like disrupting their own business model, it's harder. But I think the, your ask is like, is it irrational? I don't think it's irrational. I think there's just more uncertainty now than there's ever been.

And, you know, I think that's markets are telling you there's a lot of uncertainty. And that's why you see sort of people received from the whole category, basically. 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. 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, you know, software to add to the ARR. you typically have like an account management team or customer success team to keep churn 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. And, you know, if that annuity is not an annuity, then that math really changes. It really changes. And so, you know, because the whole idea of soft as services, you can just, you know, slow down hiring and you become very profitable because the annuity starts storing off cash. That's been the thesis of every private equity firm who acquires slow growth software-s or 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 time frames 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, voting away machines. You know, I get it. Like, 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, you know, when we first met, I doubt either of us had an extremely positive view of the future of Microsoft. You know, at the time, it was like, you know, it felt like a previous generation company. Now you look at Azure, their open-out 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.

Yes, yes. I have a lot more question. Would you like another goodness? Sure. 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. So if you want to experience the next chapter of the internal economy firsthand, join us at Stripe Sessions this April. Use the code cheeky pind for 50% off a conference pass at sessions.stripe.com. You talked about business models. Are you guys usage-based? Or yeah, how are you innovating on the business model front, or are you? 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 sort of rate for that. And if we do have to escalate 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. Yes. You know, 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. 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. Yeah, there's kind of a few lens 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 it all? Or this is just, it would not be possible almost to a fixed price contract because... I would actually argue outcomes-based is pretty different than usage-based. Okay. 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, a little Stripe GMV, however you'd value that.

but I'll use, you know, one, 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. Folklore, yeah. Forklord.org, yeah, I love it. It's like if you're an engineer, it's a fun side to go to.

But there's 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. 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. 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 know, 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, you know, homeownership over time? I think that's actually, it's hard. Yeah, yeah, yeah. But it's not. As an AI has you have a territory kind of? I think so. I mean, even because it's hard today and 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 of 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. You know, it was either your IT team or a systems integrator's responsibility to implement the software, and the job of the software can move is 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, you know, what's the same success as a thousand father's failure as an orphan? Like, 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 it. And it was like everyone's pointed at everyone else. 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, creates a strong, incentive for the software company to have skin in the game to just, you know, help you

navigate that last mile. I think so much, so many of the problems in the software industry are due to like that lack of accountability. You know, if you talk to any company's ever implemented an ERP system, it's like a multi-year process. It's invading Russia. Yeah. 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 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're going to even pay for mobile app install, you know, 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. You should share in the outcomes. 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.

the ways you might not be able to yet, but you want people to be spring-loaded to be thinking about it. 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, yeah, yeah. It's transactional. But we find, like, 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, you know, 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 of 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. I'm glad we're in our second good as. Exactly, yeah, yeah, now where we get to it, 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 short AGI? 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 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 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. In theory, they could just develop all the software. And actually many of them have tried. There's actually competitors to Salesforce at almost all 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 and 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 like 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. And, you know, I think it turns out that, you know, GBT5 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 know, you can go beyond those

lines, but it hasn't happened traditionally. And I think the reason for it is most business users, you know, want actual solutions to their problems and they want a company that serves their unique problems in a very specific and bespoke way. So I actually, I actually am extremely bullish on an 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. And so many of the startups, particularly around here in San Francisco are basically doing relatively wrote 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. 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. I agree. And I don't know, like, I'm not sure how much a long-term value is.

I think it is. You know, I think your customer... Up to this point, like, the race is led by a matter of months, right? 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 right. Like, 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, like, 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. And, you know, software engineering was the most scarce access asset in a company, and now it's the most plentiful.

And I don't think we've ever lived in that world. 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 B. And I think that'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've seen this in some corners. You know, Block announced their 45%, 50% AI layoff yesterday. and you have some companies 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.

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. And because there isn't like 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.

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. 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 a response right now. 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. 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. 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 of 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, it might be right. But I'm not sure our companies are set up to essentially absorb the benefits of AI officially 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. 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 and like, wow, AI is going to 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 the local flower shop, like if you took all the AI in the world and gave it to that, you gave it super intelligence. It's like how much would impact the flower shops operations? Like, maybe a little. I mean, I'm sure it would help.

Don't get me wrong. But someone's still, you know, clipping the ends of the, you know, stems of the flowers, 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. 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 sort of everything's in digital systems now, not even just crypto. I mean, just everything's in digital ledgers everywhere. It still doesn't touch a wet lab. It's still, you know, can't do a clinical trial. You know, you still need to get, you know, a crate from this country to that country, you know, 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. And then on top of that, I think, kind of 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. Just to push on that. So software engineering, I think we're 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 in the flower shop where just and stuff that requires really good robotics that we're far away from, that will take a while. What I'm talking about is like the there's like a... By the way, I might prefer a flower shop with a florist.

Totally. Yeah, yeah. Just to say it, I'm not sure it solves, I'm not sure it solves a problem I have in my flower shop. Absolutely. I might be wrong. I might be unique in that. Yes. But I think 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. And it just feels like a current course in speed were not on track to get those productivity improvements.

I'm not sure I'm right, but I would argue thinking about it by department rather than by processes where it's off. We talk about the process as well. But 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. 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. 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 or silent. We're all just installing co-pilot.

And copilot's great, by the way. Didn't mean to insult it. But it's not actually like, yeah. Yeah. And to be here, like, that's the kind of thing we're doing where, and obviously companies, 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 like, there's no such thing as an AI lawyer.

Instead, there's like improving your commercial. contracting, like that is a thing that you can tend to. And even more narrowly, like, pick one domain of commercial contracting and solve that. And I actually think those are truly solvable. And I think the companies that, you know, 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, you know, my hope is that as the applied AI market matures over the next years, will see. see kind of a step change in productivity.

Yeah. There is a canonical way to build a Silicon Valley company. 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 a grown-to-market organization and you have these pipeline coverage ratios and you have the product marketers and all this kind of stuff. But I found it interesting how similar so many Silicon Valley companies are to each other because they've all learned for 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 withers than without. How is that canonical template for building a company different post-AI than before? Yeah, it's a really interesting question. One is, I've always believed in the primacy of tech leads over engineering managers. I both Google and Facebook where I spent some of my early career, both did this well, where, you know, in like a product review, you weren't just talking to a manager, you were talking to the tech lead and PM who were a product manager, who were building the product. Whereas if you went to, you know, 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 a 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. Those people are truly worth 1,000x other people because it's relatively easy to find someone as 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, like, combine that will, I think, end up, you know, 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. And as companies grow, you tend towards more specialization, you know, just because the person

who's sort of the jack or drill 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 da-da-da-da-da-da, 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. 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 in the future. 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 it's right. 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, 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 the next, you know, eight hours of my life? And, you know, that might be a novelty that wears off, but I think it's really exciting. And so I'm hopeful on the product engineering design side, you end up with these hyper-high agency people, who really deeply care.

I really like the way you said it actually. It's right. It's not just customer problems. It's like care, period. Just care 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 roles these people in? I mean, hyper-generalists. Yeah, like kind of product managers, but like sometimes maybe without a product, like minister without a portfolio, they're just doing stuff, but now they can do much more. 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. Yeah.

You sway back again. You know, that's welcome to. Just one more reor. Yeah, exactly. I'm a middle manager now. And I think that it is interesting if these people become extremely important. 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. 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. 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?

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, what's a joke? And I worked for Salesforce six and a half years, and I don't think my mom does what Salesforce does. you know, so, and so to have something that was not really just like a business issue or a technology issue, but like a sort of in the mainstream, I realized I didn't love that very much, you know, like I don't mind.

Yeah, exactly. You know, I like, I'm like a builder. I like to build, I like to build things and have people use them. I know, it's not sort of funny and reductive. That's what gives me joy. So the one thing I realize is the, you know, the conflict of it all. However, it turned out, like, you know, victory, defeat, whatever it was. It just didn't, it wasn't something that, like, filled my bucket very much. What do you make of the fact that in all these kind of head count debates, Elon is now running Twitter with 80, 85% fewer people? I think Nikita Beard tweeted recently that all of Eng Productions Design as Twitter is 50 people. And, you know, maybe it's really impressive. Yeah, it's been a little flaky and things.

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? I don't know. I haven't followed as much as sort of the, like, I didn't see that, that tweet as an example. You call it tweets still. Sorry, yeah, I'm retro, I'm old-fashioned. 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 has been an individual contributor engineers knows that the size of the team does not produce like linearly greater outcomes.

Yes, yes. 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? It's a two pizza box sheet sort of thing. But then do large tech companies under-rate? this phenomenon? Like, do they pay

lip service to small and power teams and two pizza teams, but extras that maybe they should be doing more? I think their companies largely act somewhat rationally. I can't remember who the CEO was, but it might have been the Ripley and 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, you know, at the end of the day, you can be clever but not smart. And, you know, you might be so clever to think, like, 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, like, who cares that you are clever with your, you know, two pizza box team or two-person team or, you know, one AI agent team or whatever it is? When someone said, we're going to have, like, a X-billion-dollar company with one person, I think that might have been right, but it's not... Maybe you could have had a $10 billion company if you'd hired a bit more. That's right. And I would actually argue the more specific thing is if all of a sudden,

for some 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 company. And so I think at the end of the day, you know, when you're building a business, especially one that's in hypergrowth, which, you know, successful businesses and 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.

You know, you wouldn't think of 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. Laws worked as HP. Oh, I didn't know that. That's interesting. 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 them, both like the layouts of offices all look 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

push button. I don't need to worry about it. And so I think, I do think it's the right question to ask from every technology company. Yeah. After being on the Twitter board during the Elon Takeover, you were then on the Open AI board when Sam got fired. Have you considered that you are the problem? You are bringing the drama. I came in after the drama there.

Oh, right, you joined after. Oh, sorry. I was brought in as the mediator. I see. Yeah. Post. Okay, yeah, okay. Your hands are keen.

I'm aligning my reputation here. Yeah, I was actually on the side of it. but I got a phone call, was it Saturday or Friday after? And, you know, basically my understanding was I was the person that both the existing board and Sam agreed upon to kind of help mediate the situation. What have you learned on the Open AI board? 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 is it's very easy to grow Not cynical but like you know You can look at you know Open AI Google Anthropic and say like who's you know Who's model scores better on this leaderboard To actually go in and see this company Where every single researcher is trying to make safe AGI

And not come out of those 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. And that's really interesting as well, just because... Well, and I mentioned the fiduciary duty is you have a duty to the mission. Yeah. And that is really clarifying and interesting as well, because when you're making decisions

and you realize, you know, 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 You think about it very differently through that context And then the other thing was because I was brought in you know After that crisis

There was three people on the board on the other side of that when I agreed to temporarily be the chairman I still still there Funny that we had to grow the board essentially from scratch and so that was really interesting to you know just to think about Normally you add one board member at a time. This one was like, do you have a bulk rate? You know? We're going to build a board. So you really think about, you know, spent time with the other two board members just really thinking about, like, what is 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. I guess that was really rewarding as well. Just sort of building a board, not from scratch, but effectively from scratch.

Last question. What are your AI predictions for 2026? 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 needs.

I'm not sure it's going to make. You're not excited about an dimensional manifold space. Exactly. And, you know, it won't quite be like the 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 and science that actually can improve the human condition. So I'm really excited for it because I think we'll contextualize why so many of us are excited about this technology in a way that sort of captures attention.

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 autonomous tasks. 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 long, write code by hand and that might seem almost it's sort of funny that it sort of feels obvious right now 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 a 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

sookane 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. So the year of agents across businesses and just people finally getting their kind of clause type agents. And then, yeah, all code written by AI as well. Yeah. It's a good set of donations. Right. Thank you.

Thanks for having you.