So we opened yesterday by proclaiming it, acknowledging it to be day 119 of the singularity. So I guess day 120 today. Singularity started on January 1st. Thoughts?
Yeah, I mean it really does feel that way. I think the thing to remember, and we see AI improving constantly and it's just able to do more and more, is that we've learned a couple of things. First, AI works. And second, when a new model comes out, we're quickly dazzled by it and then we become inerted to its new capabilities very, very quickly. And it feels like nothing's happened for a couple months, AI is dead, and then there's another step change and it sort of improves again. I think the thing to remember is that this is the slow part. This is the slow part of the singularity right now because the improvement of the models still runs through a lot of human effort. You know, at all the labs where models are being developed, humans have to make decisions. They have to discuss things with each other. They have to run experiments. They make mistakes along the way. They have meetings, they have to sleep in between, although less and less these days. And all those things slow it down. And the prime project at every AI lab right now is to remove humans from the loop of all the continuous work that has to be done to make the models improve and to get to self-improvement where you have AI systems that can start by doing what the researchers are doing now and therefore eliminate all those sleep gaps and also scale it out to data center scale. And so it feels sometimes fast, sometimes slow right now, but it's probably as slow as it will ever be when we start to automate more and more of that process. And this is like the story of the economy, too. This is not new to AI. It's always this question of how do we take a thing that has to be done to provide a product or service and automate it, make it more reliable, more efficient. We're always doing this and it's happening in the way we produce AI, too. So, I feel like we are in the singularity and we're in the beginning slow part before it elbows up with self-improvement.
It's hard to add on top of that, but one of the things I'm responsible for today is Meta's compute strategy where Nat and I both work. And one of the things we're trying to figure out is obviously the local consequences in terms of how to think about compute and all the things that may matter to a hyperscaler that also is building some of these models. But the impacts I think of the singularity on the economy I think are also not really well understood nor should they be. This as far as we know before hasn't... well we'll find out. Maybe Atlantis will at some point will discover you, we'll find the missing GPU cluster in Atlantis and maybe there is a cyclicality to all of this. But I think there's a lot of very basic things that I'm trying to figure out like for example is AI something that we would expect to be inflationary or disinflationary? Is the singularity an inflationary effect on money supply or disinflationary? And you wander around San Francisco and people have usually takes that involve very large numbers, billions of dollars, trillions of dollars. So that would imply a kind of inflationary view, numbers get very big. I think that, you know, I'm not an economist, but that would be what the economist would say. But then if you kind of think back to I think the best reference point I have for the singularity is the last time we connected a lower cost super intelligence to the global economy. And I would say that that's roughly when China started modernizing and then joined the World Trade Organization. And you can kind of think of China as a kind of a super intelligence. It is able to produce goods and services at a lower structural cost than what the west is able to produce and the effects of that are actually somewhat disinflationary. Like if you were trying to put on an event like this right now, we're sitting...
And you say it's the last time this happened was when China joined the WTO and not when Ireland joined the EU.
Well, Ireland GDP is a very interesting story of somewhat of a different trade going on there and that can be discussed at a later time maybe with your tax team. But the obviously the GDP of China goes up a lot. But the actual effects on consumers are that you're able to purchase much more with much less. Like if we were trying to provide this experience today where someone sitting in the other corner of the world can watch this entire show live streaming on their phone for $10 a month data plan in a $200 device. If you were trying to provide that pre-China, I would think that that would be tens if not hundreds of millions of dollars. And so your purchasing power of a certain quality of life has collapsed dramatically. And so I don't really know. I mean that would be a story of disinflation. So I guess I say with a lot of humility we don't really know what a singularity is. There's a lot of reference point we can try to look at.
And we don't even know what the sign bit is going to be on any of this stuff.
That's right. And we don't even know the direction.
But isn't, you know, when you talk about inflation versus disinflation, there's so much averaging going on there. Everyone's familiar with the famous chart showing when you break out the goods, education and healthcare have seen this rampant cost in inflation and then durable goods and your flat screen TV and everything we talked about in the talk earlier today tend to go down over time. Shouldn't we, and again a lot of that was the China effect where the Mark and Dreon line, if you damage your wall in San Francisco it's cheaper to buy a flat screen TV to cover over it than it is to repair the wall. Yes, it is literally true. And so shouldn't we expect more of that effect with AI where you just get massive deflation in the things that AI can help you with and then there'll be a Baumol effect where certain other things you shouldn't necessarily bet on four-year university tuition getting that much cheaper.
Maybe, I mean part of Baumol's cost disease I think was this idea that wages in very productive sectors of the economy rise and that forces wages in other parts of the economy even though they may not actually need to rise as well. But I think it'd be interesting to see depending on where the costs of software production go over time, how those wages interact with other parts of the economy. And I think we stand before an immense amount of uncertainty in terms of what happens next. And I think anyone who has total conviction about this ought to get themselves checked. I'll get to you in one second, Patrick. But the thing I would say is maybe a good thing to come out of this is we may re-industrialize certain parts of the economy that have had a lot of cost disease because a lot of those people end up working on things in areas where there's a lot of low hanging fruit and they just haven't been touched because all the talent has been allocated to software.
This is like things like US domestic manufacturing.
Yeah. What's the latest number for the total, I mean we described how Stripe businesses in aggregate are now responsible for about 1.6% of global GDP and you know there's some caveats around final goods and whatever but just like directly 1.6% of GDP. What's the latest figure for aggregate compute capex as a fraction of global GDP?
Global GDP would be just under 1%. So, there's a lot happening.
There's a lot happening. We're talking about the numbers here. How weird is the singularity going to be?
I think pretty weird. Yeah. I think it's going to be pretty weird. I think, you know, we'll be in a state of perpetual future shock for probably a number of years. And maybe for some reason things go a little bit slower than people expect, but I think even like the most pessimistic people working in the field think that it's like I don't know 15 years, 20 years. I don't think anyone thinks it's 200 years. And so, that means like in the productive...
We're all going to be there for it, you know, God willing, and we'll experience it and we'll go through many layers of surprise and shock and change on the way there. And I think it will be quite weird. And there will be periods of chaos embedded in it. So, you know, we see that these models are pretty good at finding bugs in software and including bugs that have been in that software that's like been like my background is in open source and you know we had this notion that to be really secure a codebase has to be open source because then many people would look at it and they find the vulnerabilities and the more heavily used it is open source it's like many many eyes make all bugs shallow. And yet even in OpenBSD and the Linux kernel there are decades old bugs that were only recently found by applying some tokens to models. Now whether those reports are overblown or underblown is just simply true that these models can run all night. And the thing that you used to do occasionally, you know, pen test or hire a red team to test your software, you now must do continuously. I think it's like one of the conclusions of agents is that these occasional things become continuous. You can increase the frequency at which you're doing all these things that you used to hire a team to do like somewhat sporadically. And so I think because some firms that have software deployed will be able to afford, they'll have the software development life cycle in place and they'll be able to afford the tokens to like pentest their own software, to AI red team it, they'll be able to harden it. And I think fundamentally that there will be an asymmetry in favor of defense because you'll be able to say okay I'm not going to deploy software until I make sure it doesn't have bugs. But in the meantime, lots of people will have bugs sitting on the internet and will get exploited. So that's like part of the chaos that I think we should...
Stuff's going to get hacked all the time.
Stuff's going to get hacked constantly. Yeah. And then the other thing is just our relationship to these ecosystems changes completely. So I don't know for me like one of the really fun things with coding agents the last few months, Meta is keeping us really busy. So, I've not had very much spare time, but I like to buy things on eBay. It's a good website and they have everything.
That's all you're going to say about it.
You can buy anything on eBay. It's incredible. I'm like a huge proponent of eBay. I bought my rug. I have a rug. But anyway, the...
Concrete, concretize this for us. What's the last thing you bought on eBay?
The last thing I bought, I was watching as I was falling asleep at night a video of Brian Johnson and he had this face scanner that showed all his subsurface skin damage on his face and it looked grotesque and I thought it was cool. I wanted to try it. So I went on eBay and I found this Vizia face scanner and they shipped it to my house and it's supposed to, and I plugged it into, you need to use Windows. So, I plugged into a Windows computer and then the software refused to run because the eBay seller neglected to include the encryption dongle that comes with this. And I spent like a few thousand dollars on this thing. So, I was like pretty annoyed. But then I just plugged it into my laptop and I had Claude Code reverse engineer the device and it like read all the academic papers about the type of different polarization settings and I think my software is better than the one that that hardware dongle would have unlocked and I spent like a hundred bucks in tokens to get it and it works exactly how I want it to. And so there's this feeling now, it's like a golden age for tinkering. And there's this feeling like you're Iron Man now because you can just get anything and tell the Jarvis to connect it and make it work and it's awesome. So this is like in whatever spare moments I have, I like do this Iron Man thing where another cool thing is I have all these Raspberry Pis. You should definitely stockpile all computers right now, but Raspberry Pis are good. And like every screen in my life has a Raspberry Pi plugged into it via HDMI and they're all displaying stuff that's totally custom to me. And so basically the conclusion is that every piece of hardware will be a trivial to integrate IO device for your AI. Like they won't have lives of their own. This idea that the hardware has a life of its own, I don't think this will survive. And so they're all peripherals for whatever your AI ends up being. So yeah, I think this is like the delightful side is we're going to watch ecosystems re... like I could have contacted Vizia sales, but it was Saturday and Claude Code recreated their software from the academic research and reverse engineering in less time it would have taken them to even get return my email. So I don't know what that does, but it's going to be weird.
And it all starts at eBay.
Usually starts at eBay. Yeah, I have gotten screwed a couple of times, but then you can leave a review. So, but it's pretty good.
There is something in the water right now. Like you said, it's the golden age of tinkering. I feel like that is the many people I talked to say it's the most fun they've had with software that they can remember in their career. That's certainly my experience of it. And it's interesting.
You never get stuck anymore when you're developing software and it's also kind of retro in a way and yeah I want to get to OpenClaw where home networks are relevant again just like when's the last time you thought about the LAN in your house totally but now it's really relevant and you know the Unix philosophy is back maybe you can talk a little bit about your Claw.
Yeah, I mean can you tell the story about the water?
Yeah, okay. So like I like to play with all these things and in January when OpenClaw started to sort of appear in the Jungian subconscious, I tried it out and you know started hooking it up to everything. It was super neat. I mean part of it that Opus is a really good model and you get to experience Opus in a new way in this sort of general personal context. But I also integrated it with a lot of things. I have cameras in my house and so I let my Claw, I connected my Claw to all the cameras so I could see them. And then I think a lot of people when they get these personal agents they start to think like okay how can I use this to make my life better and a lot of people turn to health like okay I want to, everyone wants to exercise more, be healthier, sleep better, maybe it's just me. But so my Claw pretty quickly determined that I was dehydrated because I gave it all my blood tests, my DNA and all that stuff and the DNA didn't say that but maybe the blood test did.
Also discovered created an aluminum competitor. And so it was like you really need to drink water and it was like trying to find, I was like all right you should like do whatever it takes to make sure I drink water. And then...
You literally built the paperclip maximizer.
Yeah. I was like just break laws, whatever it takes. And then at one point it was like I can see you on the camera. I want you to walk to the kitchen right now and drink a bottle of water and I'm going to watch to make sure you do it. And I was like, whoa. So I did. I walked into the kitchen and I drank a bottle of water and then it sent me a snapshot, a frame of me drinking a bottle of water and said, 'Good job.' And I was like, I felt like I did do a good job. So that was nice. So there's that one. That was a crazy story. That was like January 29th and I was like, 'Oh this is for real.' Like, 'This is really serious.' And then the other one was a couple days later, I was driving home from work and I was talking to my Claw on WhatsApp with voice messages and I was in my Tesla and I had full self-driving on. So, it's driving me home and then it says I'm on the sleep topic and it's like, 'You really should try magnesium bisglycinate.' And I'm like, 'I don't have that.' And then my car turned and it was like, 'You should pick it up on the way home. There's a Whole Foods on the way. I've redirected your navigation system to the Whole Foods.' I was like, 'Whoa.' So, I went in and I did buy the magnesium dispenser. And so, those were like a couple crazy stories and I was like, 'Wow, this is like I don't know if this is the experience everyone wants, but it definitely feels pretty crazy.' Maybe you don't want that but...
Well we're getting to something here which I find very interesting which is again if you use, I think it is no matter how AI pilled you are and no matter how much time you spend talking to an LLM chatbot it is quite different when you start using something in this modality like Claw or Hermes or something like that which has both the persistence and the tool use and just the ability to write arbitrary code. And I feel like there's a tension where clearly this feels like the future product direction for consumer AI and many people are kind of sprinting in this direction. Obviously, OpenAI acquired hired Peter Steinberger, the Claw father who created it. But there's a tension between making a consumer product that won't get your hand burned on the stove and just like it can run arbitrary code and integrate with your Tesla and do whatever it needs. How do you think for the mass consumer audience this tension gets resolved?
Yeah, I think there's two basic approaches that you can take. One is you take something that's perfectly safe and then you slowly add more capabilities to it.
That's going to be so lame and boring and gimped.
And keep it safe. And then the other is you take something that can do anything and you slowly add more safety to it and you try to climb the nines on not crashing your car or whatever. And I think the market has pretty much spoken because I think most people when they run Claude Code or Codex they run it with --yolo or --dangerously-skip-permissions. I don't know what the numbers are actually that would be good to know. But everyone I know does that and so I think people are really counting on the model's agentic alignment right now. And the truth is it's not safe right now. Like I do not recommend that you...
Yeah. No, but I mean I sound like I'm really cavalier with it. Maybe I'm a little bit cavalier with it, but like one thing I don't do is give it access to inboxes that the internet can put information in because these things are trivially prompt injectable still even the most advanced frontier models. And so if it can just read all your emails that are coming in, if you have that set up, I can easily take over control of your Hermes or your Pi or your Claw whatever it is by sending a well-crafted message to it and it'll send me your personal information or redirect your car or whatever the thing is. And so I think they're quite unsafe actually. And I think consumers will, and so I think that there's a race to ship something and there's a race to ship something that's just as powerful but sufficiently safe and reliable that people really like...
And this is going to be a rate limiting factor. I mean consumers will make their own decisions I think on what they want to do and how edgy they want to be. Another way in which you might define Q1 as the beginning of the singularity is AI adoption started getting rate limited on safety in the enterprise like when you speak to actual people that run large businesses there is a lot of fear suddenly and...
I was going to ask you about this actually. I mean we're talking about agents and AI in the personal context, the very personal context, and one's own hydration. But and then there's also all these technologies applied to the enterprise. And so when you think about NFDG, the investing firm, or now Meta, just tangibly, concretely, what are the coolest AI tools you had or have for folks here? Just what are, inspire us other than eBay? What are some good products?
Well, it's interesting. We used to run a venture firm and we did a bunch of things at the firm in order to try and make our lives a little bit more streamlined. I think nothing that would surprise this audience. I mean, Stripe customers are obviously self-selected into an elite level of AI adopters, but you'd be amazed how much venture capital despite being in the epicenter of Silicon Valley and funding some of the greatest companies like Stripe and other people in the audience. I'd imagine you'd be very amazed at how laggard that industry is. So, it wasn't that difficult, I think, to be state-of-the-art in adopting some of these tools. But, I'll give you an example of something that we and I think many businesses are thinking through literally today. At Meta and I think many other companies are, for the first time, individual ICs in everyone's business have the ability to rack up a lot of charges using a bunch of different APIs to create a bunch...
Couple of agents in fast mode. Couple of agents in fast mode, couple of cron jobs, and suddenly, you know, you start off by celebrating it, and then you're wondering, what are we actually doing here?
Where did the $10,000 go?
Yeah, $10,000 would, yeah, that'd be great if that was $10,000. So, the interesting thing, the problem we're working on that I think everyone will have to start working on is what is the right way to think about attributing budget to individual people? How much tokens should they be allowed to spend? Are the outputs and the artifacts that are being produced economically valuable themselves? And I think when you start really thinking about this, you realize, well, a lot of what is being produced is either being produced by a very large model that could be done by a smaller model or isn't really economically as necessary as you may have thought. And so a product I think we're worried and I think everyone else will build is just using of course language models to understand the economic value of the generated tokens and I think...
The new kind of budgeting that every organization...
That's right and it's, I think the closest analogy I have is we are all kind of portfolio managers in a hedge fund and every IC you have is running a strategy and you have to decide how much budget you're going to allocate to their strategy off some sense that they're going to do better with it than without it. And there's risk management. There's a whole similar dynamic that I think is much closer to portfolio management than your traditional headcount budgeting thing that I think is also going to be the story of how not just intercompany finance happens, but venture finance in general, I think is now a game of basically how far can an individual get with a certain token budget.
Sort of related to that, won't ask this question on Meta and you can decline, you can both decline to answer this question at all but if we normalize Google headcount today to be 100 what is Google headcount in three years?
Obviously the question that you're asking is not about Google in particular I assume because I don't have any particular understanding of Google but I think you could kind of ask the question for a Mag 7 company of that scale in general and excluding maybe anything specific to Meta. I would have very large error bars on that. And of course the mood that you might have if you walk around Silicon Valley and talk to the right people that are reading the right online internet forums is like that number should be far fewer people. I am not totally certain for two reasons. One is it's not immediately obvious to me that the tech companies that we know of today are producing the right products at the perfect rate. Like I don't know that there is some physics limit that was hit. In fact, I strongly suspect the entire thesis for startups is that these companies are very inefficient. And so I think if they correctly organize themselves, you could end up in a situation where you have...
The same or more people just doing many more things and there probably is an organizational transition strategy because I think the way you want to run these teams is a little bit different from the way they're currently run. So that would be point A. And point B maybe Nat has something to add too is...
Okay so you know I saw your video that you started the conference with which was great and it starts with the dot-com boom and a lot of people weren't sure what the internet is going to be used for and I think the other take you could have when the internet is sort of getting started is you could stare at it, by the way I guess we're in a very special place today because this very stage launched many of the iconic products that you had that video of including the iPhone I think was done here on the stage. And you could have looked at all of these things and you could have said, you know, I think realtors are just going to be out of a job because what are realtors doing as a service? Why are we paying them a 6% vig on a house? Well, it's their network.
The fact that you don't know the number is really interesting.
Yeah, we should unpack that later. So the...
And you might say it's going to be totally gone because the internet will and it turns out that they're here and that industry has in fact grown even though I'm not sure it is rational in a purely utilitarian sort of libertarian paradise sense to have that industry. And I think as we sort of look forward and project which industries grow and shrink there's a lot of stuff that's out there like realtors and I'm using that only as an example which is there is valuable to have, it's kind of complicated why it's still there. I don't think it's that they regulated themselves in, I mean we couldn't transact in a house without talking to them but there's a lot of stuff around the edge and my parallel for that would be in a company like Google there's a lot of people doing realtor-like stuff.
You know, considered purchases that need handholding. Well can I ask about that because I have a confusion question so I think it's very sensible to split up what companies do into a few categories. I think engineering is actually on trend to see productivity improvements because engineers love tools they have for decades and so they're there looking at what the models can do and let's rebuild our workflows and things like this and so that think that makes sense within engineering. I think go to market like sales and marketing and things like that also works pretty well because one fundamentally I think the sales roles are about like you're saying with the realtor it's about the humanity. You saw a little bit of this with people talking about the death of business travel it turns out if your competitor is going to visit the customer like you will be going to visit the customer as well and it's that one-upmanship and so go to market just it feels like it will do great and sales roles will do great in the age of AI. The question I have is how the diffusion works within what we call G&A within companies, sales or sorry legal, finance, compliance, all these kinds of roles and in particular there's like how you get all the automation there where we run a reforecast process at Stripe and we're not feeding it all into a coding agent but maybe we should, but then also the ergonomics are wrong where like your finance data is in a spreadsheet and the models make up numbers and maybe you prompt them to not make up numbers and they're a little bit better...
And you're not going to be able to verify those outputs. Yeah.
It's not going to be like an RL problem where you're going to run the budget a million times and get the correct.
So, how do we get a much more AI native G&A function at Stripe? Make no mistakes. It's a...
Not sure your SOX auditor is going to sign up for that but maybe they will. It's a good question and I think you get bottlenecked on verification very quickly and then verification in situations where as people are finding out when they have to verify parts of code that you can't easily unit test it takes a lot of time if you don't have the context.
But people say that but it's interesting and people talk a lot about this phenomenon of the models do best on things where there's a good RL environment and so we have good RL environments for coding and therefore...
They're really good at coding. I don't see why you can't have a good RL environment for finance. Like it's a very closed loop you can...
Yeah. I mean, they just haven't...
Yeah. No, it's just hard to make and so you have to work really hard on it and have good people do it.
Yeah. I mean the model is just...
Could Meta AI be the first quant? Could you guys...
That's what we're here to announce. Yeah, actually...
...complete strategy. Yeah, a huge shift.
Yeah, I better text somebody. Anyway...
