Please welcome head of financial services at Anthropic, Jonathan Pelosi.
Thank you. Thank you for being here. On behalf of Dario and the entire team, we really appreciate it. And for those joining us on the live stream, thank you for dialing in. In the room, we have the CEOs and CTOs and CIOs of the world's most influential financial services firms. And carving out your morning to be with us is not lost on us. So, we promise to make it worth your while.
A year ago today, when we launched Claude for Financial Services, we made a promise. We said, 'This technology is going to help change the way your people work, your business operates, the products and services your organization would be capable of building.' And today, that promise has arrived. And what changed isn't just new models or new features of which there have been a lot. But it's the answer to the question every one of your boards has been asking: Is this hype or is it real? And we're thrilled to announce that today it is real.
12 months ago, a demo has been brought to life today by helping reconcile the books, process claims, build new models, transform the front and back office, and that's what we're here to talk about.
So this morning, you're going to have a chance to hear from our product leaders, our research leaders on where this industry is today and where it's going. And you're going to hear from your peers at Goldman Sachs, JP Morgan, and AIG on how they've driven this transformation within their own organizations in a safe and responsible way.
The intention of this morning is not to do a pitch or provide a laundry list of all the new things coming out. It's to have a candid conversation about where the industry is and where it's going and how we at Anthropic want to help partner with you to get there. And there's no better pair to kick us off than our very own CEO Dario Amodei and the CEO of JP Morgan Chase Jamie Dimon in conversation with Andrew Ross Sorkin. So, please join me in welcoming them on stage.
Good morning everybody. We have so much to talk about this morning about where AI really lives, how it lives within the world of finance, the issues around cyber security, around compute, about the role of government and regulation in all of this and how it's going to affect so many of us. So here's where I want to start the conversation. And Dario, I think we spoke at DealBook a couple months ago, but even a year ago, we were talking about just the acceleration of all of this and how fast it would move. It has moved fast, but if you were to sort of think about what it looks like 6 to 12 months out, even from now, in a very real world sense, what does it feel like to you?
Yeah, I mean there is this dynamic where you know you can write down a line on a curve like the kind of exponential and we've been right every time, right? We've been right about the way the business grows. We've been right about how the capabilities grow as measured by various benchmarks. But there's this inherent unpredictability to what does it look like when you actually make a model of a particular capability? What products do you actually build on top of it? Like what is the texture of what it plays out in reality. So that's my first caveat that there is a fundamental unpredictability to this. But I will say that I think we're going to see a few things that are kind of predictable from the existing trends. Anything that you do today, there's going to be more accuracy, more reliability, right? As the models get better, they do new things, but also things that they do now where we have worries. Hey, did it actually get that right? It wrote this research report, but can I rely on it? Right? We were just talking about, you know, both of you kind of using Claude for, you know, basically to do research on something and like it's really plausible. It's right most of the time, but how can I be sure? Those error rates are going to go down. We're going to see also more agency, more autonomy, ability to do things end to end, right? Ability to instead of, you know, here's this thing I would farm out for an hour, here's this thing that I would give to a team for a week, two weeks. So I think those are the things we're going to see. Maybe one more would be pulling together different pieces, right? Searching the web, searching different financial data, ability to do various other things. So I think autonomy, reliability and accuracy, and doing things end to end.
Jamie Dimon, how do you use it yourself personally and how are you using it in the bank in a material way?
Yeah, so first of all, welcome everybody. Dario, thanks for having us here. Personally, this weekend I actually went on Claude Code and I asked the following questions. You have financial services. I'm the CEO of a bank. Kind of give it context. I want to look at five years.
It doesn't know who you are yet.
Well, it actually keeps records of who you are. So, it actually probably did. But I want to know about asset swaps and treasury bid-ask spreads and liquidity in the markets and investment grade and risk and that. And just in 20 minutes it created this huge dashboard with all the backup and all the research and it was very accurate about what I wanted and stuff like that. So the bank first used it in 2012 I believe when we were having some of these mortgage problems and back then I call it more advanced math. It was like machine learning where we would notice patterns and run through lots of data that a human being couldn't do and notice those patterns. That was early on and once I saw it for the first time I said holy, I want to do that ourselves. So we built our own department. Now obviously we can't do it, he does, but so we have a lot of people deployed doing it. Hundreds of use cases: risk, fraud, marketing, design, note-taking, document review. And it's just starting. I mean, it'll be customer errors and as you get better there'll be second order effects we haven't thought of yet. There'll be databases we can use in completely different ways. And it also like you already mentioned, we have internal data, we have external data, we got data we buy, there's data in the web. It'll be able to do all of that and just get to a different level of managing risk or managing customer personalization, etc.
One of the things that you announced just yesterday is a deal with some other firms including a competitor of yours as well as Blackstone to effectively put AI into portfolio companies in private equity all across the country. What does that look like? And one of the questions I had for you actually is why do that? Meaning do you not think that there's enough other companies that could go do it on their own or is this about trying to get this to accelerate how quickly it happens?
Yeah. So I think the dynamic I would describe this is this is an example of a familiar dynamic that we've seen with the technology which is that the technology itself as I said is on this exponential and kind of moves at lightning pace. But large enterprises aren't like that, right? Even if they have CEOs who really get it, who are visionaries as Jamie does, even the ones who are most visionary, you have to control this company of thousands, tens of thousands, hundreds of thousands of people. It's like, you just have to educate everyone on where the technology is, how to use it. And by the time you're done educating them on a certain level of technology, the next model has come out and made new use cases possible. Some pitfalls that were present are not there before. Some things are more accurate. So this is an incredible pace of change and we're held back. Our ability to commercialize this technology, to gain revenue for this technology is held back not by the power of the models and the economic value it can generate in principle but that sort of diffusion through the world. It is both the case that that diffusion is faster than we've seen it for any technology previously in the world but it is still much slower than what the technology itself is capable of and how the technology is moving fast. Now we have our own go-to-market team for doing this but think about it, Anthropic is roughly a 3,500 person company, our go-to-market team is half a thousand going on a thousand. Some of the companies that have revenue the same order of magnitude as us have 50,000 person sales teams. You can't hire a 50,000 person sales team overnight. Even if you could, we need help. And so this idea that there are parts of the economy that we can leverage, that we can use to say, hey, there are some common lessons here in how to distribute this technology to your portfolio companies and make you more productive. This kind of company that we're putting together can have a lot of staff that specialize in this that don't have to work directly at Anthropic that can focus on the diffusion through society rather than on the inherent technology itself.
There is likely to be a trillion dollars spent in this space over the next year which is astonishing if true. Do you say as somebody in your business this makes a lot of sense to you? Do you think this is crazy? What do you think?
So I think you're referring to data centers. This year is 450 billion, next year 150 billion. The way I look at it is that in total it will make sense. If you want to try to pick the winners and losers you will have a hard time. So there will be losers in that. There'll be winners. There'll be people saying I told you so and stuff like that. But the technology itself is so powerful, it's worth a trillion dollars of investment. And the investment is way beyond that, by the way. It's in chips, it's in wires and it's in hardware and all these other various things. But you know, technology tends to pay for itself, just not in a straight line.
You've long talked about it as a cone because we've talked about your spending and people say, 'Is this going to make sense?' And you've said it could go perfectly or it could go terribly. Things have changed though even in the last three months.
Yeah, I mean I wouldn't so much say that things have, that framework has changed so much as it's kind of illustrated this framework. I think what I said when we talked four or five months ago was we've had this kind of 10x rate of revenue growth in the past and we don't know whether that'll continue or whether that'll slow down and so you have this 10x uncertainty. If you look back at the first quarter of the year, the annualized rate of growth if you annualized it, which it's not going to continue annualized, but just if you annualized over that quarter, we didn't get 10x, which I thought was the biggest we could get. We got 80x. So like the cone is even wider than I thought. So I think focusing on whether these investments make sense, it makes more sense to focus on wow this is a situation of actually radical uncertainty and it is actually very hard to predict where we were prepared for 10x growth and then we saw for one quarter and we don't know what's going to happen in the quarters after this annualized 80x growth.
If you're as successful as you want to be, and this I'm curious where you land on this Jamie too, there are so many companies in this room that rely on other software companies and there's a real question right now about the future of software, whether there's any moat around software. Do you say to yourself that all of that software that you're using at the bank right now or even you may be using inside Anthropic ultimately will not be that way?
First, I just go back to this growth he has. I would separate a little bit consumer from enterprise because it's not clear to me how consumer is going to play out. A lot of you probably use, you can use Gemini, you can use stuff, you can use it for free that may be completely sufficient for your requirements. Enterprise, you look at it always a little bit more like I'm making an investment, what I'm getting for it and if it makes you better off you're going to make the investment whether it's hardware, software or machine learning or adding salespeople. And your question was the other question was what?
Well, I think the real question, the other big question is what happens to all the other software companies in the world.
It'll be both. A lot of these software companies, and this creates another little bit of a risk, they're adding agents to what they do. So, it may be easier to take their software, add the agent, use that than try to redo it. But, I could imagine like I just mentioned what I was talking about doing. There are a lot of data sets out there that I can go do directly. I don't have to go through third parties. So, I think it'll be a little bit of both and it'll be what the product is, who they're servicing. And the other thing is a lot of smaller companies, they're going to buy those services from the vendor. Like there are a lot of small banks all use these same three vendors for their main statement system and stuff like that. Those big vendors will probably deploy my guess is Claude into those banking systems and they'll buy it from them. They just can't afford to do it themselves.
Where do you land on that?
Yeah. So the framework I would use to think about it, think about SaaS in general but I think it also applies to SaaS as applied to financial services in particular is look, there's this concept in engineering called Amdahl's Law which is that if you have a complex process with many inputs and you speed up many of the inputs like 50% of the inputs, 90% of the inputs you're then kind of held back by the ones that you're kind of not quite able to speed up. And so I think in terms of moats that software companies have, it's going to be a little bit the same. I am a maximalist in terms of the model's ability to write code and write software. I think if your moat is our software is complex and difficult to write and we can write it and others can't match it, I think that's going away. But if you look at a company, any company of any significant size is complex, right? They have a lot of different sources of moats. So what we're about to see is a rapid change where many of the moats go away but some stay around and there are new moats that kind of arise in a world of AI that we can't kind of conceive of yet. And so when I think about it my view is look, software as an industry because it's getting cheaper to produce software because there's going to be so much more of it like in aggregate that's going to go up. It may not come from incumbents necessarily, right? I don't know what will happen to the group of today's SaaS incumbents as a group. That's more indeterminant. I think individual SaaS companies, it's very possible for them to lose market value, go bankrupt, completely go bust. But it depends on the response, right? I think there are incumbents today that are going to see very clearly we have a lot of moat here. The moats here are going away. We're really going to pivot and we'll do better than we did before and there are others who are not going to pay attention who are going to be blindsided and they're going to have a really bad time.
Okay, I want to get to a big headline because it is the issue of cyber security and Mythos in particular. The White House has been anxious about it. There's meetings between the bankers and the treasury about the possible cyber security risks. When you think about this, I mean, there is a bit of a freakout going on. Is it warranted?
You know, I wrote my chairman's letter this year and I had a little section, cyber is our biggest risk. It's been our biggest risk for years. And I said, and it'll be made worse by AI and like a week later, Mythos comes out. Yes, there is a very heightened risk. I think Mythos did exactly the right thing. Dario and Anthropic to lay it out, give people a chance to study it, understand the vulnerabilities, come up with plans so we can handle it. It's very important. I mean, I think that in big picture, AI is going to cure some cancers folks. It's going to save lives. It's going to make life much better. This is a temporary thing because these code, also new code you're going to write, all the good stuff already built into it. So there's a transitory period where we have to make sure we understand and again it's not just for us. So think of, I'll split into a couple of categories. JP Morgan own applications probably can handle it. Open source. We don't all need to burn the same tokens fixing the same open source. We can all fix that for everybody.
You're saying the open source that's inside JP Morgan.
No, the open source that JP Morgan uses, but it's the same open source you use or the same open source Goldman uses, etc. And then there's third parties. There's critical to non-critical. And how do we get that fixed? And we should use that to help everybody. It's not about the big banks, it's all critical infrastructure, by the way, just to make sure we understand the risk that this new technology.
Is it a one-time transition or do you see this as something that is just an ongoing arms race?
I would say one big one-time which may take a year or so. But then after that, well, you'll always have to be adjusting to the new models.
Well, you're estimating this is a year process.
I don't know yet. Yeah. So, a few points. One, I would say I completely agree with Jamie the risks are very real. This is why we took the actions we did. But they're also in some sense not that surprising. If I go back to my whole way of viewing the world of AI, which is the exponential, we've been seeing warnings of this for a while, right? A year ago, we first started looking at using Claude to review code for security vulnerabilities, right? It wasn't that good back then, but it provided some value for engineers who didn't know anything about security. Six months ago, we found the first attempts by Chinese state-linked actors to conduct attacks on US tech companies with Claude. We sent someone to testify in front of Congress about this. We said, 'Look, this is an emerging problem now. It's going to be a big problem soon.' Three months ago with a model before Mythos, we found a bunch of vulnerabilities in Firefox. That's an example of the open source Jamie was talking about. We disclosed it to Firefox. We announced it together. It was something like 20 vulnerabilities. Now with Mythos, we found almost 300 vulnerabilities in Firefox and thousands, probably by now tens of thousands, behind closed doors. So I think the risks are very real. In terms of the government response, a couple points. One is I kind of actually commend them for apprehending fairly quickly that there is really a serious risk here. As was reported, I had a very good meeting with Secretary Bessent where we talked about the cyber risks. The secretary is getting very good advice from people across the banks and financial services. And so, I think they're taking the risks seriously and I really commend them for what they're doing. But because this is emerging so quickly, there's inherently a scramble to figure it out and I think what we have now is a bit of an ad hoc process, right? Something that almost mimics regulation but lacks the consistency.
What do you see the biggest danger being right now? What is the true threat that just everything can't get patched fast enough and that simply by identifying the problems it effectively allows the bad guys in?
Yeah. So, we have other companies and eventually open-source models catching up, right? So, I would guess other companies are maybe one to three months behind. The kind of major other credible US labs are maybe one to three months behind and the Chinese models are maybe six to 12 months behind. So I think we have roughly that amount of time to fix all these vulnerabilities. We've identified these tens of thousands of vulnerabilities. The reason we haven't announced many of them is of course only a small fraction have been fixed. We fixed 300 with Firefox. We're going to do periodic updates on what has been fixed but not everything has been fixed. Because if we announce something without it being fixed, then the bad guys will exploit it. And so I think the danger is just some enormous increase in the amount of vulnerabilities, in the amount of breaches and the financial damage that's done from ransomware on schools, hospitals, not to mention banks, financial accounts, pharma, various other things. Now to answer the question Jamie asked, I actually think if we handle this right in 6 to 12 months, which is the time in which we have to handle it because the Chinese models will catch up by that time. If we handle this right, we could be in a better position than we started in because we fixed all these bugs. There are only so many bugs to find, right? The depth doesn't go forever. And if we're rewriting all of our code with models like Mythos, we can use Mythos to write code that's inherently more secure by design. So I think there's a better world on the other side of this. This isn't just about fear. This is about a moment of danger where if we respond to it correctly and I think we've started to take the first steps then we can have a better world on the other side of it.
One of the worries that the White House has expressed is about compute. That there's not enough compute in this moment for the government to be using this software and be able to find all of its bugs and everybody on the commercial side to be able to do that as well.
That is actually a straight misconception. It may even be a misconception that the government thinks that. I don't know, but it's definitely a misconception that it's true. Mythos consumes a very tiny fraction of Anthropic's compute. It's no issue whatsoever to increase the amount of compute by 3x or 10x. There's no contention between tokens for the government and tokens for the private sector. Now, where I think they do have legitimate concerns is the idea that if we open up Mythos to too many actors, again, JP Morgan was one of the prime headline partners, but Jamie is aware that there are a lot of people within the bank. He has to be careful who he gives Mythos to. If we open up Mythos to hundreds of organizations, then there's this danger. On the other hand, we need to be fair to everyone. We need to fix things as quickly as possible. We're hoping eventually to get to the point where we have safeguards where we can release Mythos to everyone and block the cyber problems. So the question of how slow versus how fast to go through that gradual release process. It can't be infinitely slow because again we have the Chinese models are our pacing. They're six to 12 months behind. That is I think the thing that the government is grappling with that we're trying to work with them and I think that's the area where we're all working with this, we're all grappling with it for the first time and I was grateful to see that the government is starting to think about a more streamlined process that would apply equally to all of the different companies in the space. I think what we need here is regularity and predictability because the last thing we want is an arbitrary process that is unpredictable for the private sector. Like it's good to do these responsible releases. Mythos was kind of one of a kind, but we have several different companies in the space. They all need to be able to make ordinary commercial releases every month, right? There's an ordinary commercial race and that race needs to continue but just subject to some guardrails to make sure we don't cause cyber havoc.
Jamie, you've been highly regulated.
Let me give you a perspective a little bit. So the banks, the large banks have already gone together. They have a lot of working teams. You're going to hear from Lori Beer who's our CTO, triaging the issues, looking at them, looking at all the critical paths of failure, all the infrastructure we rely on including Fedwire and all the things like that. It's got to be done by industry. It should be done eventually to help all the banks. So, it's not about the big banks, it's about protecting the system. So, we're already looking at what we need to do to help smaller banks, etc. Every industry needs to do it. The government can't do all that. The government can help us by there are certain laws we have to deal with. There are certain requirements they can help us by not overburdening us when we have, you know, eight regulators asking us the same damn questions every day. So, there are a lot of things they can do. But, I agree. I applaud the fact they know it's serious. They've taken in, I know they spoke to other industry groups and at one point all the industry should get together. Of course, look at all each other's notes. What did we do? We learned and we don't all need to do the open source code. So there are just a lot of things can improve the process. And you mentioned the thing about patches. In the old days, you put out a patch, people had a week or two to fix it. And now you say it's got to be like minutes. Okay. It's something like that. So you got to just change the process by which we go about these things, which is doable.
Dario, just speak to this though because there is a headline just the past 24 hours about a potential executive order to create either some kind of working group or even some people have described it as an FDA that would approve each of these new model iterations and whether you think that's a good or bad thing.
Yeah. So, I think this is an area, whenever there's an emerging technology, we got to get the balance just right. And I think there's some bad examples on both sides and I think there's a good example in the middle. Like I think what I've been saying for a while is, we don't want a wild west where you can just do anything, right? We chose to do what we did with Mythos, but there was literally no law, no requirement preventing us from just offering this thing in the wild with no safeguard. So, should there be? Yes. So that's not going to work. Now, the example you gave of the FDA, I think the FDA slows down medical progress a lot. So, that's a cautionary tale in the other direction. What I think an example we could look to where I think they've gotten the balance pretty right is the automotive industry. Where, we understand that automobiles are a technology with immense economic value. We couldn't live without them. If we hampered the automotive industry, it'd be horrible for the economy, horrible for human welfare and human life and the ability to live our lives. And yet we understand that you can't just start a car company, make a car, without any, hey, are there brakes on this thing? Does it do that? And so we need to little by little kind of grapple or grope our way to some process that lets the industry operate expeditiously, is fair but puts guardrails on the most serious things.
