Anthropic CFO Krishna Rao on Compute Strategy, Scaling, and Frontier AI Returns

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My guest today is Christian O'Row, the CFO of in-throbbing.

The center of our conversation is how he navigates the decision around procuring and allocating compute, which he describes as the canvas on which everything else gets built.

We talk about what he calls the cone of uncertainty, the three chip platforms in-throbbing uses fungibly across training and TPUs and GPUs, and the daily meetings they run to allocate compute between model development, internal use, and serving customer demand.

He explains whether it turns to frontier intelligence, keep getting higher, especially in enterprise, and how in-throbbing thinks about the line between platform and application, and whether it's used to build their own products like cloud code.

Christian has such unique seat watching one of the fastest growing businesses in history, and he is generous in sharing what he has already learned since joining the company two years ago.

Please enjoy my conversation with Christian O'Row.

Christian, I have been so excited for this conversation because you get to see from the inside one of the most interesting businesses in world history.

That may be the most interesting time in world history, at least if you're a technologist or care about technology.

One of the things that fascinates me most is this question of compute that you have to deal with all day every day to keep part of what you do, it's a key part of what these companies are doing, and there's just this whole revolution happening.

I'd love you to just start by explaining what it's like to have to deal with that.

I understand at one point you're having a daily meeting about how to allocate to compute and to who and why bring us into that part of your life because I think it's right at the cutting edge of what's going on.

The compute that we procure, it's the lifeblood of our business.

It is the most important thing in the company, it's like the canvas on which everything else gets built.

So the decisions we make and how much compute to buy are some of the most consequential and hardest decisions to make in the entire company.

Think of it this way.

If you buy too much compute, you go out of business.

If you buy too little compute, you can't serve your customer and you're not at the front here.

The same thing.

So we talk a lot about this cone of uncertainty, but the idea of just these purchases that have these real world implications, you can't just go out and buy a giga lot of compute and have it delivered next week.

You have to really think ahead to plan for this, and so we really take a very disciplined approach to how we think about it.

So we look bottoms up, we model what we think demand will be, obviously we sometimes get that wrong.

Think about the compute we need to stay at the frontier, and we really look ahead and try to estimate that.

And then as we go out and do these deals to procure compute, flexibility is really important to us.

And so we build that flexibility into the deals themselves.

We build that flexibility into how we used to compute as well, because the way in which we bridge from a position we are today to where we want to go when the business is growing exponentially is to use that compute as efficiently as possible.

I would say I spend 30 or 40% of my time on compute even today.

What does flexibility mean?

And then example, it means a couple of different things.

Number one, we use three different chip platforms.

So we are customers of Amazon's training and chip, Google CPUs, and Nvidia's GPUs.

We use these chips, fungibly.

If you think about the compute we buy, we're using it for model development, we're using it internally to speed up our own product and model development.

We're also using it, obviously, to serve customers across those three chip platforms.

We're using compute for all of those internal and external uses.

And that flexibility took us a long time to be able to do that.

We've invested in that over multiple years to be what I believe the most efficient users of compute amongst any of the front-year labs.

And that's not something that just happened overnight when we started using TPUs.

I think it was third generation TPUs was the first one we used at scale, people thought, oh, well, you're crazy.

Everyone's using GPUs, we aren't using GPUs.

And we've invested very heavily to be able to use that compute incredibly flexibly.

And then we look across the different generations of those chip platforms and use each generation for the best workload internally.

And so we really built this orchestration layer that gives us that flexibility to use all different types of compute.

And in doing so, we also are able to get the most value out of it.

Am I thinking about this in the right way that something like Kuda that has been a part of Nvidia's story for a long time now that allows you to do a lot with the underlying actual hardware that you want to sort of eat your way into being as close to the bare metal as possible.

That's part of this flexibility in being able to control as many of the variables as you can.

Is that the journey that you've been on?

That's part of the journey for sure.

But it's also been actually pretty collaborative.

So we work really closely with the Enterprise Labs team at Amazon to help to influence the roadmap of these chips because we believe what we're doing is really stressing the limits of what these chips are capable of.

And that means that like a dollar of compute inside our organization goes further than I think it does anywhere else.

Importantly, we basically want to utilize each chip to its best purpose within the company.

So that does mean that we're building our own compilers.

We're really building things from the chip level up in order to have that customization and that flexibility to use it internally the way we think will generate the most ROI.

Can you explain this cone of uncertainty thing?

I want to ask about all the component parts of this.

But that feels like a really key starting point of overall frame for how to think about both sourcing and then the uses of compute.

Can you just explain what that concept is?

When you're building and growing a business exponentially, really small movements in monthly or weekly growth rates result in compounding very, very different outcomes.

And so as we're thinking ahead, even with our revenue growth, it's really hard to predict this business.

And it's really hard.

I think humans mostly think linearly and you think incrementally, I've been at the company for two years, that's something that's paradigm I had to break for myself to stop just thinking linearly and think on this exponential.

When you're on this exponential, again, the range of outcomes starts to be really, really wide.

We look at a range of scenarios and we look at different points in that cone of uncertainty over a one to two year period and then we kind of work backwards from that.

And what we want to do is we want to be at a place where we can obviously still be at the front here.

That's the most important thing to be able to serve customers and then to be able to have enough internal compute to accelerate our employees.

It's interesting if we were to say to our employees, you can't use our models anymore.

We could serve billions of dollars of revenue with that compute that we allocate to employees internally.

But we want to take a long-term view and a long-term perspective on that cone of uncertainty because we want to range towards the top end of these outcomes, but we have to plan for that.

And as we go, that's how we think about buying compute in a discipline way.

The most important thing is what happens if you are at one point in the cone of uncertainty, but you've only bought compute for a different point, that's where this compute efficiency is something that really is helped us out.

Can you bring us into the room for the conversations around the trade-offs between those?

I'm so interested by those three buckets of training, research, internal use, probably speaking, and then serving customer demand.

And I believe you might think, okay, it's a third or third allocation or something.

How much does that range around?

What are the trade-offs?

What does that discussion like kind of ongoing basis?

In addition to meeting about compute procurement, we meet a lot about compute allocation.

I think what's important is it starts with a place where our culture is a one that's incredibly collaborative, and that informs how this conversation happens.

So there's not fiefdoms.

It's done in a very collaborative, not a zero-sum way.

There's a level of compute for model development that we will not go below.

Even if it means it's harder to serve customers or we have to do kind of unnatural things when it comes to that, we want to continue to make that long-term investment in developing the best models because we think the returns to frontier intelligence are extremely high.

And it's extremely high, especially in enterprise.

That kind of puts a floor on the compute that's allocated to model development.

And then as we think about the internal use of compute, it really helps us to speed up that model development and to speed up finding those compute efficiency multipliers that really get us more from each dollar of compute.

So when we're talking about it, each team is kind of representing what they would do with that compute.

And then we have a really open and frank discussion about how we think about ROI.

And because we can allocate that compute so dynamically, we can make changes.

We can make adjustments in that on a relatively short time present.

The efficiency thing is so interesting to me.

I'm curious if you have a sense of how much more efficient you are versus your own internal benchmarks from a year ago or something or versus others that you have some sense of how efficient they are.

How do you measure what efficiency means?

There's a couple different ways I would think about it.

Some of model perspective, I think the analogy people have when these new models come out is they're kind of like cars.

You had a sedan before and then you might have the higher inversion that sedan and you're moving up the chain.

And I think that is true in terms of model intelligence.

The place that analogy kind of breaks down a little bit is people think, okay, I'm going from the sedan to the sports car.

We get much less fuel efficiency, I'm not going to buy this sports car for the gas mileage.

In our case, we actually see huge improvements in capability, but also model efficiency.

So if you look at going from Opus 4 to 4, 5, 4, 6 and now 4, 7, they're not equal, but each one of those leaps to a new model has a multiplier in terms of how much more efficient it is at processing tokens effectively.

And that just doesn't serve customers.

That also helps us internally as well because if you think about if we're doing reinforcement learning on the model, it's basically inference within a sandbox with a reward function.

So if the model's better at more efficient inference that RL is more efficient as well, we're able to do this kind of win-win where the customer is getting more capability when we release a new model.

And then we're able to serve that model sometimes again, a multiple more efficient than the prior generation.

And then when we're in between generations, we're dynamically deploying efficiency improvements in between these more step function model changes.

It is always getting more efficient over time and what fuels that is the research team.

So if you think about all these things are very connected, these various tasks and workloads that we have internally all fit together in this way of doing R&D to model capabilities for compute efficiency for serving customers and then having internal workloads that can be sped up by using the best models, sometimes models that we haven't released.

You said something really important before, which is the returns to being at the frontier are really high.

Can you just explain that in as much detail as you can?

Sounds obvious when you say it, but there's certainly been some camps.

I can use the six-month-old model and it's a fraction of the cost and I'll just use that and that'll be catching up all the time and it just like hasn't been the case.

The second Opus 4.7 comes out, even me as a consumer.

The thing you do is you switch it on or GPT-5.5 comes out, you switch on the new one right away.

I want the best.

Talk about the returns to being on the frontier and why it's high.

I think it's a couple things.

It's every time we have a new model, there's a set of capabilities that are different.

People tend to think about model intelligence as IQ, it's a single number, okay?

This model was at 110, and then it goes to 125.

We think of it differently, intelligence for us is multidimensional.

It's not just a score, in fact we find that, yes, every one publishes their model benchmark cards.

Find that a lot of those benchmarks are saturated.

We publish it too, but what our measurement is, what the customers tell us.

What is the real world capability of this model?

As we've released better and better models, what we've seen is it's not just the outright intelligence.

It's also the ability to do long cries of tasks, it's the ability to use tools or computer use.

It's the ability to do things for a genetic task that have specific value even faster.

In some sense, if you have two employees and they're maybe both equally capable, someone takes a week to do on assignment, someone does it in a day, well, that's the person if they're continuing to do that can be seven times better.

They might be equally capable at something, maybe just take longer times to do it.

All of those factor in to then how customers experience it.

And what we found very consistently is by releasing new models, the tam is unlocked in a unique way.

More tam gets unlocked, more use cases are possible.

And a good illustration of that is this last four months that we've had at the company.

We started the year with about nine billion dollars of run rate revenue, and we ended the quarter with the thirty billion dollars of run rate revenue, that kind of a change is really enabled by these model intelligence leaps, and then the products that we build around them.

And so that's what I mean by the returns of frontier intelligence are really high.

I think that's unique to enterprise.

Then consumers, sometimes you don't see that as readily.

That consumers really are pushing the limits of what the models can do.

Whereas in enterprise, like our customers are always now, we started with coding, but it's really expanded beyond that very meaningfully.

But each model generation gives you the chance to do more with it, to do it better, to do it more efficiently, and customers see that, and then they invest really heavily in more tokens with the newer models.

And we just have seen that cycle play out again and again.

And that's a court thesis of our business set, especially in enterprise.

The returns of frontier intelligence are not slowing down.

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The things that push that front here is like a sci-fi story or something from a book that I was reading when I was growing up.

It seems as though in the major labs, we've reached this point, someone on your team said recently, of recursive self-improvement, where the models themselves are building, then doing a lot of the research to do the next generation of improvement.

If I think about the frontier that you're pushing and open AI's pushing and compare that to the open source models, that maybe the gap will widen as a result of you getting their first to this recursive thing, how do you think about that?

Tell us how we should think about this idea of recursive self-improvement in the models themselves because it seems like getting their first incredibly important, because then you can continue to separate yourself versus those that haven't reached it yet.

We do see progress accelerating.

I can't speak for other companies, but for us, the scaling laws are alive and well, and we're seeing that with releases more recently like mythos.

But right now, within the company, 90 plus percent of our code is actually written by Cloud Code.

A lot of Cloud Code's code is written by Cloud Code, and so you think of this as, why do we allocate compute internally, why would we forgo revenue for it?

It's because the models themselves are helping us to build that next generation of models.

In addition to this capability leap that you would have from the scaling laws, talent is really important.

And that talent with the best models can really accelerate the development of the capabilities and we're really seeing that.

We don't really think about models as closed or open.

We think of them as frontier or not.

The ones that are at the frontier, clearly are capturing this economic value driving meaningful ROI for customers.

We are just investing behind that thesis, and that means both compute, but it also means talent to use that compute and use our own models to really accelerate the development.

And that's been something we've been doing for a long time this is on a new phenomenon, but we're seeing the fruits of that and everything we've done.

The other piece of it is to, it's not just the models as the products that get built on top of them.

We had 30 different product and feature releases in January.

The pace of that is accelerated as well, and that's enabled in part by utilizing the models with the talent that we have to accelerate ways to access this underlying intelligence.

That's kind of our theory of the case on the product side.

How do you think about this weird world where you mentioned the talent and the leverage and they're not writing code themselves and Cloud Codes writing its own code?

It seems like the last step of that would be you don't even need the talent to tell the thing what to do.

It just figures out what to do on its own and that's like the ultimate thing then just runs and it's only constrained by computer something.

Am I being too crazy about that or is that future possibility you think?

The core of our company is still a research lab.

I think it's maybe not as well understood, maybe it's getting more understood from the outside, but we're doing experiments.

We are doing things that push the limits of what our models can do and that research and that engine is upstream of everything else that we've talked about.

That is enabled by the models today.

It's not entirely done by the models.

Over time, we think that the models will get better.

There'll be more helpful in that process, but having the best talent to set the direction and to help effectively guide that process.

Not just the priorities, but some of the new areas of discovery.

It just actually makes that research talent even better.

I think of it as accentuating and accelerating the talent that we already have.

We talk a lot about how talent density beats talent mass.

And I think that's true here.

Like we want the densest collection of AI research talent and inference engineering talent.

And that enabled with the best models is a really winning combination.

How are scaling laws talked about internally?

This sort of consensus has been, you've got different components of them.

You've got pre-training, you've got post-training, you've got reasoning.

And that all of these are kind of moving at different pieces.

And today, to true wall, they would all need to fall down.

That's sort of like how the world is now conceptualizing scaling laws.

However, they talked about internally.

How do you think about them?

We look at models at various points in their development.

So we can see during a pre-training run, how does this model compare to a prior model that we did on these kind of lost curves?

And that gives us a sense for model capability.

You can do the same thing as you think about RL.

Probably as importantly is when customers get their hands on it.

What are they seeing?

Where are they identifying pain points?

And those pain points become like training targets for us.

We don't train on customer data in the enterprise side on the prosumers side.

It's only if you opt in.

But customers tell us things.

Like, hey, I wish the model were better at this.

Or I had this particular place where it got stuck.

And I could build this other product.

But the capability needs to be further than that.

What we usually tell them is build your product for that.

Because we're going to on the R&D side, improve that over time.

And so there is this connected loop.

But internally, we're always looking at different models that are being trained, different snapshots that we have.

And comparing them internally into a lesser sense.

And externally against our own measure.

And then ultimately how our customers view them as well.

And it feels like there's just no slow down in the scaling loss themselves.

Is that a fair characterization?

For us, that's a fair characterization.

Obviously a bunch of the authors of the scaling loss papers are amongst our founders.

Notwithstanding that, we can be a bit of a skeptical bunch.

We hold ourselves to a really high standard.

It's very a kind of scientific method.

And people are constantly challenging, previously held assumptions.

But from what we see, the scaling loss are not slowing down.

So if that's true, you said before it's hard for humans to be exponential and they're thinking and not linear, that continues to be true for however many more turns of the crank here.

How do you do that thing of not thinking linear and thinking exponential yourself in your job?

And for the business.

The implications are really hard to reason through.

Expensive growth rate is one thing, but exponential growth of capability.

I like don't even know how to get my head around it.

How do you get your head around it?

We think about the world as scenarios.

It's very hard to have a point estimate in this business.

And then having a very low bar for updating your current prior, basically, or your current perspective.

Because it could be the case that something a month ago is true.

That's just not true today.

And that breaks your model.

And you have to go back and update it.

And so this old, like, well, we'll forecast one supporter and we'll visit this in three months at the next board meeting.

That doesn't work for our business.

It's so dynamic that we have to always think about, oh, our models couldn't do this before and they could do this now.

What does that mean for the term?

We've seen this in coding first.

Starting with around Sonnet 3536, we started to see this really remarkable jump in capability, which was then followed by adoption and usage and revenue.

It was a little hard to predict that.

But now we can use coding as an analog for a lot of what's happening.

Elsewhere in the economy and elsewhere in our business, we kind of look at pattern recognition in our own business to try to predict what's going to happen in the future.

Literally 15 minutes before you got here, the news came out about your partnership with XAI and the Tennessee facility.

But it makes me curious about how you are canvassing the world.

That is an opportunity that you decided to do.

I'm sure there's a universe of things that you've explored.

What is the strategy for trying to get more in creative waste?

Bring us a little bit more into that.

We announced the partnership with SpaceX for their cost of facility and Memphis really excited about that.

It's going to allow us to continue to expand, especially on the consumer and pro-sumerside.

But that's just one example of us just as you said, looking for near-term compute, wherever we can get it, as the compute base grows, that near-term compute becomes a smaller and smaller fraction of what's available and what's out there.

We look at it as, can we deploy that compute that's available productively?

Sometimes, the answer is yes, and sometimes it's no.

But if we can, then we look at the economic return on it based on what it's price, what duration we have it for, where it's located, what type of compute it is and how efficiently we can run it.

So we have kind of a process to assess.

And that same process, by the way, we used to assess longer-term deals as well.

So last month, we signed a five gigawatt deal with Google and with Broadcom for TPUs.

Starting in 2027, we also signed a deal with Amazon for Trenium for up to five gigawatts as well.

It was an over $100 billion commitment and a lot of that compute is actually already landing on the land in the rest of this year into next year.

And so if you think about it, it's as bit of this layer cake of compute that's starting at different times with different capabilities when we're very dynamically comparing that compute.

It's price performance over time.

That's really, really important to us when it lands.

And what we think we can do with it internally in the business.

And so there's so many different variables.

You have to optimize for around what compute it is at what cost and over what time horizon.

But we have a pretty dynamic way of looking at near-term compute and then medium to long-term compute.

But the things we're assessing are largely the same.

What is different is just the time horizon.

But about the trade-off use of price performance, trade-out between like cost per token or something, throughput and speed.

Both are important, of course, from the customer perspective, they care about both.

Speed probably unlocks some capability in use cases that are really interesting.

And that we don't know about yet as these things get faster.

Can you talk a little bit about that trade-off in compute as you're assessing it?

As we look across three different chip platforms, we also have multiple generations of chips within it.

It could be TPU V5E and V6 and V7 and training 2, training 3.

All of them are different places on the price performance curve.

And then we importantly look at how we will utilize it.

Price performance is important because of efficiency.

Speed is also important for certain use cases as well.

So we look at the compute down to a very granular level in terms of what it can deliver for us and when.

And that's something that we do again, our compute team leads that.

But we closely collaborate across the business to say like, where do we need this compute and for what?

Okay, we might need CPUs for RL.

Okay, how much of that do we need and where and what's the nature of it?

We might need this more leading edge compute and we're going to deploy it for our best and fastest models or for training them from our perspective.

It's customer demand, but it's also really down quite granular in terms of what is each chip best for and then what will we have when?

I'm always so curious by the metabolism of in this case anthropic for new compute.

How fast you could take if I air dropped on you twice the compute that you have tomorrow.

Like, would you consume that in how fast would you consume that?

If I air dropped 10 times the compute on top of you, how fast would you consume it?

Can you calibrate us on that?

It feels like demands on limited between these three uses training, internal customer demand, everyone's saying the same thing.

Shortages everywhere, memory stocks, moaning.

Is it that extreme that like if you two extra five extra 10x the amount available to tomorrow, you just like more or less instantly consume it?

This goes back to like how we use it and the fungibility of it.

So the answers we're constrained across those use cases a year or two ago would have been harder to consume, especially like a heterogeneous compute drop in your example really quickly because these chip platforms are different and they are different.

Some are harder to operate.

Some of them have idiosyncrasies in terms of how we use it.

I would say today that getting a bunch more compute I think it would be deployed very rapidly across those different use cases.

We probably have the same allocation or calibration that we do with compute today.

But it's become a lot easier for us to spin up very quickly and deploy almost any type of compute.

And that's something and we think this is a real advantage.

One of the interesting tensions and tradeoffs that I'm fascinated to hear how you think through is between the platform approach where I build my business on top of Claude and it powers my thing versus you doing the thing that I wanted to build.

This is like the classic Claude design versus Figma or something like this.

How do you think about the right balance of how deep into the application layer you should go?

Versus just being a pure enabling layer of we're going to provide the reasoning engine and the intelligence and world go forth and build whatever you want, pay us for the API or whatever.

That seems like a fascinating internal discussion and tension to some degree.

The way I want to think about it is most of what we're building is platform.

We think that there's so many examples of where a platform can accrue a lot of value, but the customers who are building on that platform actually could even more value.

We think that's what we're setting up for today.

It's maybe akin to the early days of AWS.

If you think about the Claude platform and all the tools and services that are now built into it, because not just the raw model access, it is prompt caching and the ability to use virtual machines and Claude code being called within their or dispatch on and on.

The Claude agents SDK manage agents.

All of these are effectively, I think of as vectors to access that model intelligence for other companies to build into their own products.

That's most of what we're focused on and really most of where we think the business is going from where we are today.

We will also build our own applications on that same platform where a couple things are true.

Number one, if we feel like we have a vision into where the models are going and we can kind of demonstrate that and create customer value in that, that might be something like Claude code.

We are able to say actually a lot of what's out there in the market was developer led.

Claude code is a platform that's Claude led and we think the models can't quite do that today when it was launched, you know, a little over a year ago.

We think they'll get there and they have.

One is kind of building ahead to model capabilities.

The second is thinking about ways to like demonstrate value for the ecosystem that others might emulate.

If you think about Claude for financial services or Claude for life sciences, or even something like Claude security, these are ways in which we've composed the platform.

Again, we're building on the same platform as our customers.

That creates a level playing field.

We also think that there's so much value that's going to accrue in these areas that our customers can win and we can win as well, which is why you've seen as we've launched some of these products.

We've done them in a collaborative partnership oriented way whether that be on the security side or sign or financial services, we've partnered with the ecosystem.

So I think of our strategy as mostly horizontal.

We'll build vertical where we think we have some value to add or a perspective that's useful or a way to demonstrate to the market how we think about our platform adding value.

A lot of the value is going to accrue the customers that are building on top of it.

Our goal is build the best models and then build the products and tools and services that allow that intelligence to proliferate within customers.

How much do you care that it's just a reality that people are scared of you?

There is a sense that because you control the most essential piece of these new applications underlying intelligence reasoning engine, that may totally be true.

Maybe already is true that more of the value is accruing on top of the anthropic platform then is being captured by it.

But nonetheless, it's still scary to imagine.

And I guess maybe you could say something similar about cloud and AWS or something like that.

But how much do you think can care about the fact that some of your would-be customers or existing customers are in fact scared of you as a competitor?

Part of what is hard in this business is it's changing so quickly.

So the model capabilities sometimes even surprise us.

And so when we release models or products on top of that, there is an element of what's happened in prior waves over the course of five years, 10 years, 20 years, is happening in months now.

And when we release things, people are also surprised by it in some ways in the same way that we were surprised by it.

But I think fundamentally what we are trying to do is be very partner-oriented towards the ecosystem.

And that means that we have early access programs.

We work very closely with customers.

We listen to them about what capabilities they want.

That doesn't mean that the things we release are sometimes not moments where you're like, wow, that's way more powerful than I thought it would be.

Or I didn't realize the models would be able to do that this quickly.

Part of that is a reality of where we are in this cycle in this development of intelligence.

Part of it is also like we want to make those capabilities really accessible.

And that should accrue a lot of value to customers as well.

And customers that are front-footed on that and adopt and also ones that are building and using the tools that we offer on our platform, we think we can actually accelerate them.

And so I think some of it's a reality of frontier model development.

But our approach to it is probably a little different and more partner-oriented.

You said before, going 9 to 30 in the first quarter, the pace is so insane, which makes me wonder about pricing.

The dynamic of how to price tokens or use of the system is so fascinating to me.

Because I think a lot of people here go and say price is going to constantly fall.

But actually what's happening is it's going up in many cases.

This is true at different levels, whether it be the mythos pricing that is quite high, because it's so powerful, the cost of an H-100.

Do you know the rental price for cost of an H-100?

Looks like a smile curve.

I'm very curious why if everyone is compute constrained, why everyone doesn't just raise prices a lot to try to find what the right equilibrium is.

And so I love it just like riff on pricing.

How you think about it, what the trade-offs are, why not raise prices a lot.

The company is only a little over five years old.

This past March was the third anniversary of the first dollar revenue into the business.

And we only had a frontier model for real for the first time in March of 2024.

So the time scale of these things, it's an important backdrop as I think about it.

Our pricing has been relatively stable across high coup, Sonnet and Opus, and now mythos is obviously newer.

We made very few pricing changes.

The biggest pricing change we made was to bring down the price of the Opus family when we launched Opus 45.

If we thought about why did we do that?

It's really because we found that Opus class models were underutilized relative to their capability.

People were trying to often fit an Opus problem into a Sonnet.

We were able to serve that very efficiently from our perspective, but actually bring down the price which made it more accessible to customers.

We want our customers to generate a lot of value from it.

And they're generating a ton of ROI from our models today.

We want that to just continue because our goal is to proliferate this throughout the ecosystem.

We think we're in the very, very early innings on all of these use cases.

The best way to do that is get this intelligence in the hands of as many businesses from startups to digitally native businesses to the largest companies in the world.

Some of that means that you have to make it in a price point that's accessible and that allows them to get a lot of value from it.

Changing a pricing for Opus, you see this Jevon's paradox.

We lowered the price of it, but the consumption went up way, way more than what you would have expected.

And so because we hit that sweet spot, for customers, they were able to use it a lot more.

We had the efficiency to be able to serve it to customers at scale.

And then they were able to build that into their workloads such that when we really soap as four, six, it's a model improvement, they can slot it and we didn't change the price.

And so we think pricing stability is important.

And we also think that pricing to get that value and to see that kind of Jevon's paradox happen is really important.

The other component of this is margins and how you think about margins as a business.

Again, because this is so unbelievably capital intensive to build these frontier labs, you've got the levers we talked about with which is efficiency, priced both those things relate to margin.

Given how much capital you need, why not to say we want to have a healthy margin and set the price accordingly.

And maybe that price can come down if efficiency is better or whatever.

And I'm just curious how you think about margins as a relates to pricing and the business.

We think about what is the return on our compute spend with large.

So that is all of the different workloads that we've talked about, whether it's serving customers.

If you think of all of those are kind of in support of revenue over different time scales.

And if I serve inference, it's in support of revenue today.

If I do model development, it might help for a capability that unlocks tan that drives revenue six months from now.

And everything in between, if I do internal acceleration to launch a new product, all of these things are in support of that.

Our returns on that compute expense today are robust.

We think of it as what is the return on that full envelope of compute?

We feel really good about where we are from that perspective and we're balancing delivering value to customers, with also seeing a really, really strong return on that computer ourselves.

If you think about when revenue grows, as we mentioned, kind of in Q1, it's not like we onboarded a bunch of new compute in that time period.

We talked about compute comes based on a ramp that might have been determined 12 months ago.

This idea of a variable cost that's like on the incremental to serve a customer.

It doesn't really fit our business.

It tries to maybe fit our business into like a software paradigm.

That's not the case.

The naturality compute is supporting all of these activities.

We're really generating a robust return on that compute and that's our measuring stick.

I think it's something where we think of the compute envelope that we have as the thing that is able to govern how much we're able to drive revenue both over the short-term and the long-term.

If you're this great customer of the compute providers, what does that group need to do to be a great provider to you to help you drive that return?

We're fortunate that we have really great partners in Amazon, in Google, in Microsoft, but also with broad common and video as well.

Our ecosystem, we are the only model that's on all three clouds.

Today, we're the only model that has large language lab that's using all three of these chip platforms.

These collaborations are much deeper than just procurement.

I think that's something that's often lost.

If you think about early ship ad Amazon, our teams are deeply embedded with the Antipernal Labs team.

We are good users of training.

We've spent a lot of time in energy and work closely with the team and internally, and that's something where we plan capacity together.

We do that with the other hyper-skillers as well.

If you think about the three clouds, they're great distribution engines for us too.

We have a really, really robust, first-party business as well.

But these are multi-faceted partnerships, whether it be on developing the chips themselves, blending that capacity, serving it, and then ultimately distributing to the customers.

Each one of them has that element to it.

They're different.

In each of them has relative strengths, but we've been able to work really well across those clouds, and then obviously across broad-com and in video as well.

I'm thinking about your function, like the finance team.

I'm picturing this ROI on compute thing on different horizons with all these complex variables.

Which makes me wonder, how do you use these powerful tools yourself internally to run your group and the business?

What is the deployment of cloud code and cloud in general on the finance team at Anthropic?

This is really interesting because we were using cloud code about a year ago, and I started asking people, like, everyone just kind of taking up vibe coding, or what is it, and we started to use cloud code as almost like an assistant, a digital coworker, not just for coding tasks, and that actually was early in what eventually became co-work, that was kind of an extension of cloud code to say, what is done for a gentic software development, it should do for all of knowledge work.

But then we started to productionize that.

And I'm actually really probably spending a lot of time with our product team too.

They kind of see how we use it and get input and feedback from that.

But today, all of our legal entities, we can produce the statutory financial statements using cloud.

And yes, the human checks it, but all of those financial statements are produced with cloud.

We also have a more real-time platform called AntStats.

And it used to take a lot of time to sift through all the data, get to conclusions, write a memo about it, or publish a regular report on what's happening over the course of the day, what's driving it.

We now have a library of skills for cloud.

There's specific to finance, class I checked there over 70 of them, that everyone can kind of access through this common repository.

And on top of that, we built an MFR monthly financial review skip.

And it can produce a monthly financial review.

It's 90 to 95% ready.

And then all of our discussion becomes about, what do we do?

What are the implications?

Not what exactly happened, because cloud is not just reporting the weather.

It's also helping to think about drivers.

Why did the number change in the way it did?

And that gives you tremendous insight in the business, both in terms of this like MFR that we do.

But also on a daily basis.

And so what used to take hours to produce a weekly report for what's driving revenue, or what's driving our compute utilization, now comes down to 30 minutes.

And then we can spend our time on the actual strategic complication of the business.

We can also get it in the hands of business leaders much more quickly.

The insight engine is a lot faster.

I have a dashboard that I look at token usage reader board.

Yeah, we don't compensate people on it.

No, it's trying to token max for that.

But it's really interesting because some of the most senior people within the finance team are actually the biggest users of token.

And so it is not just the 22-year-old who joined and has a coding background and was doing that on the weekends and brought it to work.

It's also people using the tools to change how they're working.

Like I think our number one users are ahead of tax.

And he's focused on tax policy engines and automating large parts of the workloads that are happening within the team.

So I love seeing that.

I tell people, if we're not super users of this, if we're not pushing the limits of it, how can you expect customers to do that?

Just as a human, does it freak you at it all?

That first, so many examples like this.

It starts to feel like we just start doing this stuff that AI tells us to do.

In the sales example or the calendar or whatever.

And maybe that's great.

Maybe it's just such a better coordinator and wide thinker and optimizer than we ever could be that we should do what it tells us to do.

But it feels ever so slightly dystopian to me that that reality is coming quickly.

I've had examples of it too.

It feels kind of cool.

It's helpful.

But at the same time, if I really close my eyes, I'm just like doing what it tells me.

versus me telling it what to do.

It's a really interesting, just human dynamic that I'm curious for your take on.

I mean, it's slightly different view on it than that.

I think we've been able to hire great people at the company, but it is made even those incredibly talented people so much more productive.

There's a little bit of this, I think of it again like Jevon's paradox, but for labor, which is that we have people become incredibly more productive.

We've hired a lot more people because of that because there's no shortage of work to do.

And now with the assistance of Claude, people are spending less time in that MFR trying to reconcile some number, but they're actually thinking, oh, how do we reinvest this in the business?

How do we think about dynamically allocating resources?

Whereas before I'm working to tie out a number or I'm not accounting example, taking a long time to close the books.

So I actually think of it as even more optimistically that it is an accelerant to our productivity.

And that actually means that we can get a lot more done and that even as we grow the team, those people are more productive as well as they come up the curve on how to use Claude within our company.

And I think that's starting to be true across many companies as well.

I'd love to talk about investors and capital formation.

Of course, you've had to raise tons and tons of capital at the same time.

It seems as though if I just squint my eyes and think about the multiple on current revenue, it's not that crazy in terms of like, where are you raising money?

I'm so curious for you to teach us about what it's been like to interact with investors.

How you've seen their understanding of the company evolve and mature?

Where do you think the investors as a group understand it now?

Where are their misunderstandings about anthropic?

Tell us to that side of your life and I don't know what percentage it is if you're job, but it's an important part.

So I joined the company about two years ago.

We were closing our series D at the time.

That was not a straightforward fundraising.

The company really only had a front-chair model in the middle of that fundraising.

Towards the tail end of it, the FTX transaction was happening, which is liquidating a bunch of anthropic shares.

That was kind of the starting point.

And at that point, the questions were around, why do you need to have a front-chair model?

What's the returns to this?

They're also around our mission and how we approach things.

People said, well, AI safety and building a really big business aren't those things that ought.

And there were also a lot of other misconceptions on a new sales force is really small.

Don't you need to scale it like all these enterprise software companies?

There was just a bit of a paradigm around trying to fit us into a particular mold that it existed before.

Over time, it's evolved.

At the end of 2024, we raised the series E.

The business at scale to a close to a billion dollars of run rate revenue.

But the day of our first close was the day the Deep Seek News came out.

We got the close done, but certainly a ton of volatility.

As people then said, wait a minute, should I just totally re-underwrite how I think about AI and the total?

That was a series E, obviously we brought on great investors across all of these.

But people still had some of those questions, but they looked at our forecasts and they thought, I get it, you've grown to a billion dollars of run rate revenue so quickly, but there's no way you're going to be able to keep on that.

Yeah, this is not possible.

There's laws of physics.

You're in enterprise, which is great, but the adoption is going to take so much longer.

I mean, look at how long it took with cloud and how many people are still on-prem.

The business continued to prove out the thesis that the return to frontier intelligence is really high.

We are really focused on what's really happened as model lead growth, enabled by products and our go-to-market team in our distribution.

What they also saw was that this thesis of, hey, it's really important to build this transformative technology, but to do it in the right way and do it responsibly.

That had this really interesting interlink with our business that most people didn't really understand or really believe, which was that we invest in research, not just in model development, but also in AI safety research.

We pioneered interpretability, which is think of it as an MRI for the model to see inside the neural network how it works.

We pioneered alignment science, which is, you want the model to do what you tell it to, and how often does it do that, and how often does it stray from that.

Those things are important for a mission, and that's why we did them.

They had these downstream effects where it turns out if you can look inside the model, you're better at building them.

And then the last linkage, if you're selling to enterprises, we now sell to nine of the Fortune 10.

All of those enterprises are entrusting us with customer information, their data, their interacting with their employees, sometimes even interacting with their customers as well.

Those are the most sensitive workloads.

More and more of these businesses are running on cloud and our cloud platform.

When you have this investment that we made, and we'll continue to make in safety, interpretability, alignment, that actually asserts the benefit of the enterprise, customers as well, and all of our customers because they want that.

If they're going to entrust us with all that access and all that data, and the ability to work in the most sensitive workloads within their company, they want a company that they can trust.

That's not why we invested in it, but it did have this downstream effect that we've really seen proof out again and again to be a company that is both at the frontier, but one that is investing in safety and that you can trust.

We've raised $75 billion since I joined the company.

We have another $50 billion that will come into the future from the Amazon and Google deals that we that we ain't classed on.

And so that's a tremendous amount of capital, but it's a capital-intensive business, and we need this capital to support that growth.

It all goes to the fact that the business is running very efficiently.

And so the reason we raise this capital is more because of that kind of uncertainty, than it is to fund losses in the business.

How is your own perception of this perspective for a 10x growth of the business?

The first time that happened, did you personally believe that it was possible to that seem absurd?

Now, it's becoming consistent and maybe it's becoming more commonplace to you or something, but what was your own view staring at this cone about the odds of hitting a 10x type of growth so many years in a row?

When I joined the business, it had about 250 million of runway revenue, and the plan was to get to a billion, and I said, great, in what year, that was linear thinking.

Consistently, Dario has been a much better predictor of the revenue than I have.

I think we're going to close the gap over time as we get better at forecasting and understanding the business, but definitely the first time I saw it, you have all these arguments about the laws of physics and law of large numbers and this can't.

Where is the revenue coming from?

And how can it be added this quickly?

And how can customers move this quickly?

And is this even possible and enterprise?

All of those things start to get broken down over time as you see how the business works internally.

You see how the adoption curves and the exponentials that are happening.

Again, we have the exponential that's happening on revenue, but that's underlies it.

Are these many other exponentials that support that?

You start to see and believe in that more.

Now, that doesn't mean we're not disciplined and thoughtful about the forecast and how we think about the range of scenarios.

But it does mean that my thinking has lifted a lot more from linear and incremental towards leaning into this exponential and really believing in its potential and how this is just different than how other businesses have evolved.

As you've talked to investors at every stage, I'm sure every stage, every round that you've raised, there's something that is like the most common or hardest thing to explain to investors, for that they're struggling the most understanding that they're heads around.

What is that today?

I think it is this paradigm of how compute is used.

Thinking of it as not just something that is a variable cost over some time, period, but really this resource sets so fundamentally utilized.

We run workloads on one day in the morning on a chip for inference and in the afternoon or evening, we use it for a model development.

That paradigm does not exist in a company like a software company or a factory.

You can't repurpose if you have a bunch of people doing R&D and that's your R&D expense.

They can't go and become cogs and vice versa in most traditional companies.

Here, you really have that fungibility that's possible and I think that's where the return on compute is so important and I think people are beginning to understand that, but there's still an tendency towards treating it like, oh, I have to separate these two costs.

When in actuality, they're very self-reinforcing and that flexibility is actually what helps to drive revenue short-term and long-term.

If I was the force you out of your role and into an investor's seat at a great big investing firm and then I said, your job is to go grill these companies and invest in the best ones.

What questions would you be asking of the labs or companies that are building models to really get at the heart of the points of uncertainty of skepticism of things that might not make these the best businesses of all time curious from that angle how you would approach it.

First, what is the ROI on compute all up?

How are you utilizing it?

What return are you seeing today and how is that coming over time?

That's certainly a question I get asked a lot by investors, but I think it's a really important question.

These are the massive unprecedented investments that companies like customer making.

What's the return that you're generating on that?

When does it come and what is the shape of it?

I think that's one.

A second one is how your customers see ROI?

I get asked this question a lot as well.

Like, are people just using this for testing?

Are they actually deploying this at meaningful scale?

For our business, we're seeing that in spades, our net dollar retention rate is over 500% on an annualized basis.

Nine out of the fortune 10, these are real customers making significant buying this in pilots anymore.

Exactly.

On the way here, I was in Inuber and I signed two double digit million dollar commits in the car ride, which was like 20 minutes.

So from that perspective, we're seeing it when we're now being judged by some of the biggest companies in the world, the most sophisticated buyers and startups, they have choice in the market and they're choosing us.

But I think one question I get a lot is, or I would ask from the investor seat, the skeptical investor seat, how are your customers getting returned from this?

Maybe at third one is, how do you think about compute in the future and where does it come from?

Because obviously some of the places that we buy compute from, they sell the compute dollars, they may use the compute internally.

What is the balance of that over time?

So your philosophy there is be involved with great players and have flexibility.

That's right.

There's this crazy stat about AI, just the generic concept, being less popular than Congress amongst the general populace.

And it's kind of funny when you first hear, but when you really think about it, like, this is kind of fucked.

We need to solve this problem.

It doesn't seem like the general world that isn't in technology doesn't live in the Bay Area in New York does not yet feel or understand why this is good for them, just as measured by their opinion of it.

What do you think we need to do as an industry about that problem?

If we think about the transformation that's happening, there's been other transformative ways before all the way back to the industrial revolution, the internet, cloud, et cetera.

One of the things that's different about AI is it's all happening so quickly.

You can have years or decades of progress that are being compressed into months.

Going back to humans thinking in terms of exponentials versus linear, that can be jarring.

We are very optimistic generally about the potential for this technology.

We as an industry can continue to do a better job of articulating your Dario Routes essay machines of loving grace.

It's all about the potential for this technology to transform the way that we live, whether that be in drug development and curing diseases that are more mainstream, but also rare diseases.

They ability to accelerate that biological progress.

Number two in healthcare and how healthcare is delivered to razor standard of living in the developing world and in places where resources are not as plentiful.

How do we actually make sure that the economic gains are also pruring to not just a small number of people in the world, but across the world.

I think that all of those things are part of the promise and the potential of AI.

We could probably do a better job of painting that picture and we want to show more tangible results for that over time.

I think that is coming and that's one of things I'm most optimistic about.

I think on the other side though, we do want to articulate the risks.

I don't think we should just tell everyone everything's going to be great.

There are likely to be bumps on the road and so I think people generally gravitate towards more honest and balanced assessments.

If I feel like somebody's just telling me all the good news and none of the bad news then I'm like, okay, do I really trust this perspective?

That's where there's a need for balance.

Look, these are some of the things that happen when changes compressed over a short amount of time.

How do we work across commercial and government to actually come up with some of the solution sets?

I think it's about a clear articulation of the opportunities.

It's about thinking about what those solutions may be and that's not any one company that can come up with it.

We don't have this blueprint that's going to solve everything, but at least have that dialogue about some of the risks and downsides and what we can do to address it.

Then I think it's being transparent about both of those things when we talk about it.

I do think that over the long-term the opportunity is going to be significantly higher and greater than some of the risks in the downsides that will happen, but that doesn't mean it's going to be perfectly smooth on the curve.

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The release of mythos was a such interesting moment.

It was the first time many people friends of mine that are careful watching this stuff said something like this one kind of makes me scared.

So it relates back to the safety question.

Tell us the first example of you coming out and saying you want to make sure this isn't used for bad and it's maybe the first one that we are worried could be used for bad.

I'm curious what that discussion was like internally before the world heard about it.

The decision making process around it and just using that as an example to talk about the things that do scare you as we continue to advance and the scaling laws continue to hold.

One of the things about mythos is that people maybe misconstrued as just a cyber model.

It is a incredibly capable model across many, many different dimensions.

What we found was that cyber in particular was a place where it spiked.

This was the first model that we decided to release in a different way.

The way in which we did that again is consistent with our mission, our principles.

We wanted to do it in that way.

We have this phase approach to it because we think that when a model is as capable and again cyber is the thing that people focused on but there are other things as well.

We think it can be used in a positive way to patch code bases.

You've seen these examples where we had an open source code base that a prior model found 22 security vulnerabilities in and mythos then found 250.

That is kind of scary.

But that informed the way in which we released it.

We said let's do it in a phase way.

Let's do it to a group that will expand over time where we can focus on this one cyber capability and how can actually be used positively in a defensive way is opposed to an offensive way.

And we think that's a template that could be used for the future but because of this one particular area we wanted to be cognizant of that in like how we released it.

You're so big now that you like run into everything and everyone and one example of this is the government just a couple days ago said maybe there would be this new system where you have to sort of pre-approved the release of a new model with the government before it was released to the public.

Obviously you had the crazy experience with the Department of War which I'm really curious what that was like as you went through it.

Now everyone cares about this company and this technology and the couple of their companies that are building it.

How do you navigate that stuff?

Some of it's just I guess beyond your control but I'm sure you're trying to work with people as best you can.

Maybe talk about those two examples of the government now is a very relevant partner player or overseer etc.

We prioritize having a strong relationship on this because we do think that regulation has a rule to play.

We've been pretty vocal about that.

We are very America first in our approach.

We want the technology to support the U.S. as well as democratic countries around the world and that's one of the reasons why we've been working closely with the administration.

I do think that there's a balance.

You want to be able to have innovation happen really quickly and have that not be slowed down but you also want to have this responsibility framework for how these things are deployed because we've long said that this technology has implications and we should have an honest conversation about them and that includes what the government and so I think the myth does process is a good example of that.

Can you teach us a bit more about how you would describe their cultural tenants to your parents or something like this?

What feels like it really drives most of the culture?

I'm especially curious about the writing.

You hear often that Dario publishes these long essays over so often externally.

My understanding is he does that way more frequently and there's a lot of writing culture internally.

I'm trying to get a sense of what the culture is like to be in and what makes it the most distinctive from other companies.

Maybe that you've worked at or from other companies that are trying to do the same thing which are sense of the differences in the distinctiveness.

The culture is a real unique aspect of anthropic and it is something that we do talk about externally but it's different when you're in there living it.

I can tell you a little bit about some of my observations.

First of all, it's seven co-founders that shouldn't work on paper but it really does in practice.

I think they've really set the example for the culture and the things that really matter to the company.

We take culture extremely seriously.

We do a culture interview and it's not some pro-forma thing we do just to check a box.

It is a real part of the evaluation process.

So somebody could be flying colors on everything else and really really, as far as person you've met in this role, we won't hire them if they don't pass the culture bar.

And the way I would describe it, I like that frame, and how would you describe it to your parents, is it's one incredibly collaborative.

And this means that we don't really tolerate thief dumps or the sharp elbows or they'll I need to take credit for this.

It's incredibly humble.

We have this sticker on their laptops internally.

Our competitors are incredibly capable and success as far from guaranteed.

And I think that's part of how the company operates.

If we reach a milestone and something good happens, there's not confetti on the floor.

It's like what's next.

And I think it's just that focus on the mission and the alignment that is in but throughout the culture of the company.

The other thing I would say is there's rigorous debate and intellectual openness and intellectual honesty that happens where people question things.

People will express a point of view.

But then there's dialogue around it that's productive and then we'll decide on a path forward.

And then after that happens, there's real alignment.

So in something like compute allocation, we were talking about before.

People only have different perspectives on how to allocate that compute.

They will engage in a thoughtful discussion about where the returns are the highest or the best.

And when they do that, you know, and we come to a decision, then there's alignment on it.

There's not second guessing.

There's not politics or thief dump.

The other piece of it is it's remarkably transparent.

Dario gets up in front of the company every two weeks, usually writes a short document.

And he talks about usually three or four topics.

And then takes open questions from the company.

These are not softballs.

They're not planted questions.

They're just real questions that are on people's mind.

And he answers them the best that he can.

It's not a decision making form.

But it is a way for the company to get a window into how leadership is thinking, how he's thinking.

And there's debate and dialogue in that.

That is something that people really value.

It is a transparent culture.

It is one where all seven of the co-founders are still at the company.

The vast majority of the first 20 to 30 employees are still at the company.

The culture underpins the reason why we've been able to attract and retain some of the best talent in the industry.

Because we don't always pay people the most.

We are very competitive compensation packages.

But when meta and others route with these huge packages for some of the technical talent across the large language labs, I think we lost two people.

And other labs lost dozens.

What parts of the business in the culture specifically for researchers?

What do you think that's status true?

It really is underpinned by the culture.

That's not just something we feel.

It's like empirically.

When you talk to people, it's, I want to have the most impact possible.

I want to work in a place where, again, this idea of talent density, mattering more than talent mass.

And I want to work in a place that is actually collaborative.

Versus I have to fight for this one thing.

And I feel like it wasn't discussed and debated in the right way.

Or there wasn't transparency around how a decision was made.

I think it actually really matters.

Because most of our team just wants to do really, really good work.

And they're attracted to the company for the mission.

The idea of having an impact on a company like ours that is trying to develop this transformative technology, but to do it in a responsible way.

I think that really matters that people, not just on the research team, but across the company.

And that is a real advantage for us.

And it's not something that we take lightly.

We have this concept of a race to the top.

We don't always have all the right answers.

We don't always do everything perfectly.

But we want others to look at some of the things we do.

And maybe emulate some pieces of that and actually have the technology be developed in a better way across the industry.

I think people are also really attracted to that as well.

Again, not that we have all the answers, but that we can be a part of contributing and leading to how this can go well for humanity.

If we now think forward, as you're having conversations with people internally, what does the frontier feel like?

I don't just mean the model frontier.

I mean the next couple of roles of the dice here in building AI in general.

Everyone is kind of wise to like these things are powerful.

Everyone's using them.

What feels to you like the frontier from the inside?

I think it's this idea and again, it's because we're focused on enterprise and because, you know, we're really trying to change the productivity of knowledge work that's done in the economy.

I think it is towards this vision or this goal of like a virtual collaborator.

And so think of this as something that has context within your organization that can use all of the tools that are specific to you, whether they be homegrown tools or tools that you purchase.

That has memory and the ability to effectively learn from mistakes you've made, but also mistakes that it's made over time.

The ability to work over a very long time horizon on not just the task, but actual idea.

What that means for us is the model capability has to continue to grow to support that.

The products we build on top of it can unlock this virtual collaborator that we think can really accelerate knowledge work.

There's something like $40 trillion of knowledge work done annually in the world.

We think that the productivity and the acceleration of it can happen, but you have to get it in the right form factor.

This is where like intelligence is not just a single dimension, but the virtual collaborator kind of combines many of those things.

Something that's not just generically smart, but it's smart for your use cases.

What we're seeing in coding is something that we expect to see elsewhere.

For us, Cloud Code has led the way on that as well as much of the business that we have great customers that are pushing the coding frontier as well.

But then you also see something like co-work come along and start to unlock that.

Co-work is growing faster than Cloud Code was if you index them to the same point in time.

That's kind of remarkable because developers are really fast adopters of this technology.

But I think it's because the model capabilities in the products are pushing towards this notion of a virtual collaborator where even our product development today is not done by one product manager with two engineers shipping something over three months.

It's shipping daily and there's a fleet of agents that are working across the company on a specific task so everyone kind of becomes a manager.

I think the implications of that and the productivity gain that can come from that when it's in the right form factor, we're very, very early in that, but the potential for it is incredible.

I'm curious how you've had to personally evolve to be able to stay doing this.

You hear a lot about these stories, the executives have to scale with the company.

They're also getting new executives.

The business that you're at prior to this is a great business but it was a tiny, tiny fraction of the scale.

You like everyone is in this new unprecedented thing.

You talked about the example of getting out of linear into a more exponential type thinking.

That's one example of what I mean.

But how have you managed it personally?

What have you had to do?

What's been the most painful?

How do you manage your own ability to scale with this thing that's scaling faster than what we've seen before?

It's really hard.

I think the important thing is to think in first principles.

Everyone has priors when they come to something new.

Thinking in first principles and having like intellectual openness, I spend a lot of time with Tom Brown, Archie compute officer.

He was actually one of the first people to interview me at the company and I remember we went on a walk and he started to tell me about his vision for the future of the company and this is in 2024, early 2024.

And I'll be honest, it sounded crazy.

You walk me all the way home.

I remember I came in and I told my wife, I was like, this is going to be wild.

If even 10% of that is true, this is going to bend all paradigms of not just things I've seen but what most people have seen.

And it turns out that a lot of what Tom said during that walk has come to fruition.

I remember that as like an early formative thing, this walk I took with him and coming home and being like holy shit, this is going to be totally different and new, really incredible experience but also really challenging.

And then that's what it's been.

The other piece of this is just hiring great people.

I try to hire people and I tell people during the interview process.

I'm not really hiring you as like a direct report of mine.

I'm hiring you as a partner and I want you to treat it as a partnership, which means that there might be things that you and I disagree on.

I want to hear that.

I want to whiteboard it.

I want to understand we've hired people from some of the best companies in the world.

They come to this from a different perspective.

They might come to it from a hyperscaler or a large software company or from financial services.

In another lifetime, I worked at Blackstone in the private equity group.

That training is really valuable and thinking about things at a granular level and not losing that.

I'm not somebody who is comfortable at 50,000 feet.

That's just not me but you can't be at 500 feet at everything in this business.

There's too much surface area.

And so having people who can be partners in that is really, really critical.

I found we've been able to track really great people and they've pushed my thinking quite a bit.

I think the last piece is to think about how the business evolves over time and where there might be moments or analogs to things that have happened in the past.

I help lead the financing that Airbnb did in the middle of the pandemic.

Very different situation.

The business lasts 70% of its revenue in seven weeks and a brand did a show with you.

That was a harrowing time.

But it was also a time kind of without precedent where you had to think about things with a clear perspective when it was rapidly changing and there was not a good template or pattern to match.

And then on a personal level look, it is hard to balance everything, family and friends and this job takes a big bite out of all that.

But what I do try to do maybe once a week is in a quiet moment just think wow, this is really cool.

It's an incredible opportunity to work with this group of people on this problem at this company, at this moment in time.

Maybe it's in a car ride, maybe it's late at night or something like that.

Having that recognition and that appreciation is really important.

So I try to bring that to at least my life for a couple minutes once a week.

What did Tom tell you on the walk that sounded most crazy?

We talked a lot about the scale of the computer infrastructure, what models could do in a short amount of time.

I think he described a world that I would have said is kind of sci-fi.

A lot of what we're experiencing here and now have really roots in that conversation.

And so there's even more things that he talked about that probably beyond where we are today.

But I think the commonality of it was that everything is going to happen much quicker than we think and that both the implications but also the capabilities of that can change.

And then he also had like a really incredible optimism about the future that I think we talk about internally holding light and shade.

That's one of the things we say.

I came from that conversation with just a bunch of questions but also a sense of positivity about what could happen in the future.

It seems like we've been most of our time talking about because it's been the reality that we exist at the high end of that cone.

What can you imagine that would cause that to change to the low end of that cone?

If we were to do like some sort of pre-mortem on a year from now we're like, well actually we didn't need nearly as much computers we thought or something like that.

What can you imagine that would shift us meaningfully in that cone?

The capability and the use cases are playing catch up to where the model is.

We are talking about humans in larger organizations with a set of tools and practices and things that they've been doing for a really long time.

Change is hard.

To accent that that diffusion hits a wall or slows down or something like that that could affect the rate of change in terms of revenue growth.

The scaling laws slowing down or not holding.

We don't see that but can't say that with 100% certainty.

I think that would be silly.

We certainly believe in the trajectory but the model capabilities leveling off would be another thing.

Third is how we think about being at the frontier.

Today we're at the frontier.

I think we're defining the frontier of a agentic AI.

We need to stay there.

It's a competitive market and we're going to continue to invest in the technology and the compute and to go to market that's required to be there but that's not guaranteed either.

To finish on an optimistic note of those are the things that could cause us to go to the low end.

What are you most excited about?

You have a privileged seat.

You sort of get to literally see the future because it's happening inside the business before those outside the business see it with that perspective and in that seat.

What are you most excited about?

I really think that the biotechnology and healthcare outcomes that can come from this technology are the things that I'm most optimistic about it.

We may live in a world where you're diagnosed with a disease that is not curable but in your lifetime that cure can be found much more rapidly and you might not die of that disease.

A lot of what we're doing today is helping to speed up the drug development process.

A lot of the paperwork and clinical studies reports and things like that that are needed to be done.

AI and our solutions in particular are helping to rapidly accelerate that.

I'm really most optimistic and excited about when it goes further back into drug development and drug discovery because humans are capable at research but if you think about these molecules and proteins they're so complex and such small changes have such big implications for the outcomes.

AI is perfect for that.

What can happen when the labs throughput goes up 10x or 100x and we can run that many more experiments.

Probably get better results faster and that can be something that helps people around the world and it doesn't have to be limited to a small set of diseases or disorders.

It can really go much further down the chain and so I think that has the potential to greatly alter the way that we live and the way that we interact and that's really exciting to me.

I hope you're right.

It sure seems like we're on that trajectory and it's quite a future to imagine.

This is so much fun.

I feel like we covered so many interesting aspects of the business when I do these as the same traditional closing question.

What is the kind of thing that anyone's ever done for you?

I have a brother who's five and half years older than me.

We live in California when he went to college and he got into everywhere he applied to and he was going to go to medical school after that and I didn't know any of this at the time so he ended up going to college in state.

He did it exceptionally well kind of years later that I kind of had to pull this out of him but in deciding where to go to college you know we were solidly middle class.

This was like 25 30 years ago.

The financial aid packages weren't as robust as they are today.

A big factor in his decision I found out was wanting to give me the opportunity to go wherever I wanted that was six years out and who knows how I would turn out.

A big part of his decision, a big factor in his decision was giving me the opportunity to have choice.

I didn't know that 12 year old me or 13 year old me would have never understood many years later I think that's something that was incredibly kind and is something that I still kind of hold with me today.

I've done this like 600 times or something I've never heard an answer of that type that's awesome.

I love to meet your brother.

Christian thanks so much for doing this.

Thanks for having Patrick really enjoyed it.

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