SaaStr 2025 Ai Stage

Ram. Sa. It.

It. It's. Sa.

What they're building at Anthropic is so.

Compelling and this crazy new model every week type environment.

It's this rapid iteration which is so important when you have a team that's high energy and customer obsessed really learning the customers end up being your best salespeople. Bessemer is one of the longest standing venture capital firms in the world. This is an unprecedented moment. Where does value accrue? There's a lot of cool demos, hype, things that get one time usage, deciding where you want to invest. You maybe make those decisions a couple times a year.

I started the first application of a large transformer language model to write code. People were pretty skeptical that it would even be useful. Since then many millions of people are using these tools every day. The utility much greater. I think that's only going to continue.

Partner bessemer venture partners talia goldberg. Head of startup sales anthropic kelly loftus. Machine learning cursor jacob jackson. Co founder features and labels corcom yurtseven.

All right, thank you to Saster for having us. It is great to be be here. I'm Talia Goldberg, I'm a partner at Bessemer and I'm very excited to do this panel with our portfolio companies and our friends at Anthropic, Cursor and Fall. So before we jump in, if you guys just give like your quick bios, that'd be awesome.

Awesome. Nice to meet everyone. My name is Kelly Loftus and I lead the startup sales team at Anthropic. I've had the privilege over the last year and a half to working and scaling our startups team and so working with a lot of founders like yourself. Previous to that I was at Stripe for five years working with startups and enterprises and then at a really small HR startup before that. One of my favorite things is working with founders and enabling them to use AI in their workflow. So very excited to be here today.

Hi everyone, my name is Jacob. I first got my start in AI making tab 9 back in 2018. Then I worked at OpenAI as a researcher and then did super maven about a year ago Talia led our seed round and then around eight months ago we joined Cursor and so I've been working there on machine learning since then.

Hi everyone, my name is Gerkam. I'm the CTO and co founder of Falcon. FAL is a generative media platform. We host open and closed source image and video models in our platform and expose them as Easy to use APIs to the end users using our inference engine. Yeah, awesome.

Well, I love having the three of you because all of these businesses are basically enabling this crazy wave of AI and developers. They're always leading at every era of innovation and that's very much the case here. And we're seeing that across LLMs, across coding, across image and video. But all of these companies are also totally breaking the norms of what we've seen with growth and in the startup ecosystem in prior eras. And so I want to start this off actually by sharing a conversation that I was having with Gorkum while we were driving up here. So we'll go a little bit off script. And Gorcom was like, gosh, uvcs. The metrics are totally broken. Like all the metrics, all the questions we get asked, they're wrong because the businesses look different today. And we have these SAS metrics that looked at things like Carr and gross margin like this and had very clear, good, better, best metrics. And lo and behold, we have three wildly successful companies that have sort of broken the norm. So Gorgum, tell me to start what is broken and then maybe we can chat a little bit about what metrics actually matter for all of you.

I think the biggest difference now is companies are growing much faster. I think it used to be triple, triple, double, double or, you know, if you tripled in a year, that was considered gold standard and now it's, you know, so many companies are breaking up, going from zero to, you know, 50 million, like really, really fast. I think that's the biggest, biggest difference. But also no one in AI really has 80%, 90% gross margins because the way we sell software kind of change. Before, if you are selling a SaaS product, the marginal cost was very little to sell to another company or to sell another product. But with AI, the new gross margins are lower because it comes with a cost to sell to another person. So that means everyone has less margins. Maybe that changes over time. Models might get cheaper and if we can have like people have stickier workflows, maybe the margins go up over time. But currently it seems like everyone has less margins than traditional SaaS. But everyone is growing like crazy. And there's like other implications of this to how people build sales teams. Seems like there's so much demand for AI that people don't need these massive sales teams and they can get things done with much leaner teams. Maybe AI itself is helping some of that. Or maybe the way this type of software is sold is going to be quite Different. So someone has to sit down and define what makes a good AI company in a different way than what makes a good SaaS company. And on the way here I asked Talia, maybe Talia should be the one doing that. It would be a risky blog post.

But this is a good to do. But I want to touch on two things you said. One, we'll talk a little bit about all of your team structures and just some tactics on how you built up like go to market and sales. But before we do that, you mentioned this concept of wow, like the gross margins are lower and it's because the cost to serve each customer. Also it's not, you know, effectively zero as it was with the SaaS model. It's different. There's a real cost and that is there's a really weird dynamic that that creates which is that your best customers in some ways become your worst customers because they're causing a lot of cost in your system. And I think as a result from, we've seen a lot of experimentation with pricing models and you're seeing this ideas of like value based pricing and cap, you know, maybe more usage based pricing, maybe. Share with me from Anthropics and Cursor's viewpoint, because I know Cursor has been experimenting too with having a more usage based model, not just the 20 or $40 a month. How are you thinking about that and what do you think this looks like a year from now or two years from now?

Yeah, it's really hard to predict. I mean if you look at where things were a year ago, one year ago there was no Sonnet 3.5. And look at where we've come since then, where just a huge number of tokens are flowing through that model and where are things going to be in a year, two years from now? It's really hard to predict. And as Gorkham says, it's not like traditional software where when you receive $10 from the customer, you have to spend 10 cents in AWS costs to provide that. These GPUs are expensive and they have a real footprint in electricity and heat. And that's not going to change. The way I would think about it is just, you know, you look at the value that's being delivered to the customer and then you look at the stack that produces that value and you look at, you know, the total value being created by this technology which is, you know, large and quickly growing. And then you, you just think how can we increase that value and how can we be part of that supply chain that Takes the electricity and converts it into, you know, something that is useful to people. Yeah.

So do you think there could be like, if the average software engine engineer. I don't know. Let's make up a number. Let's say someone, you know, gets paid $150,000 a year. How much do you think you the you could charge for cursor in a year from now or the best users, the highest value users?

That's a really good question. And you know, when I first started selling developer tools for, you know, a flat price of $49, it's like, well, how much does this need to increase your productivity to be worth it? It's like, you know, 0.01% and it's worth it, Right. I think when you consider it relative to a developer's salary, you know, I think many people have been accelerated more than 2x by this technology already and the technology is only going to get better. So I think comparing it to the developer salary is the right way to look at it.

I also think the other thing you have to also think about and what we're hearing from a lot of our customers, both consumers and enterprises, is people right now, and I see this happening for the foreseeable future. Just want optionality and choice. So do you want predictability with how much you're going to spend on a monthly basis? Here is a SaaS subscription. We have that option. Or is it actually, hey, I really care about using a fast, cheap model or I really care about the workhorse and just being able to provide everything from a free model to a $200 a month one to here's a choice of models that you can use on the API side of things. And that's just what we've heard too from developers, is they want that optionality, they want the choice. They're still figuring out exactly where to use all of these models and what use case works for what part of their development workflow.

One thing we realized, I think this is true for Cursor and Anthropic's case too. Instead of models getting cheaper, yes, maybe running the same model got cheaper, but people trained much bigger models that are much more expensive to run now. And people expect to use the best model. So running inference in general, maybe 100x in cost. This happens for us in our use case for video models like yes, the image models we had a year ago. Now it's super cheap to run, but now because we have much more demanding video models, the margins even got actually lower because it's so expensive. To run these models, people demand them. So the inference costs got much larger.

Well, I have a saying at Bessemer that I started saying a year ago, which is that cogs are the new cac, which is that you can spend a lot on cogs, but it means that you also can't then be spending a lot on CAC and customer acquisition. Because if you have low margins and really high acquisition costs, that's tricky. But the good news is all of your products, they kind of sell themselves. And so it's a little bit of a trade off. But it is funny, I thought a year ago we're on this cost curve of models getting cheaper and cheaper. And so I anticipated that the margins of some of our companies would actually increase and that it was okay that they had low margins. And it turns out they've stayed really low. And they've stayed low because of exactly what you said, which is the cost to serve. The models are getting better and people are using them more. But that equation hasn't helped.

Advancements in software are always faster than investments in hardware. So maybe the next generation of chips are going to make things more cost effective. But you know, developments on the modeling side is much happening much, much faster. So it is getting more expensive to run the best model if.

Yeah, well. So Kelly, when you joined Anthropic, I think how many people were on the go to market team?

Yeah, Anthropic itself was about 250 people and there were less than 10 people on the go to market team.

And today how many people are there at anthropic?

About 1300 at anthropic, probably 150 or so on the go to market team. So crazy scaling over the last year and a half.

That's crazy. And can you share with us one thing that has been really interesting to think through is like how do you structure your go to market teams? Like, what did that look like early on and how has that changed? And if you were to think about a founder in the audience that's just starting their journey, what advice might you give them?

Yeah, I'll first start saying by it's hard. When I joined, we did not have the concept of quotas. It's just hard to pick a number on what you want to actually measure each rep by. And so what we did instead is we, thanks to our founders, we have a really strong mission of we just want to, to build really Safe models and LLMs. And so with that in mind, one of the big pieces reading between the lines is how do we Also continue to build this and get really good feedback from our models. And so what I did when I started was let's just build a team around feedback and doing everything that is for the better, knowing that this team is going to scale from 10 people to hundreds one day maybe. And so really focusing on that and keeping that in mind. And we still don't really have quotas, we have shadow targets. It's really hard and difficult to predict exactly what is happening. The adoption is fast. A lot of this is driven by the model intelligence, which you cannot predict over a long time period. So it's been extremely difficult. But really rallying people around the mission and being super clear about what we care about is getting feedback on our models and continuing to work with partners to push the model capabilities forward. And that comes from a lot of the startups we work with.

So no quotas at Anthropic.

No quotas at Anthropic today. That might change everything in this space. I feel like as soon as you say it, it's already outdated, but just shadow targets today.

Okay, so no one got, you know, no one got a commission for selling to Cursor.

No one did.

And actually maybe just quickly would love to hear the structure for you too as well.

I have a funny story about this actually. Beginning of this year we were looking to hire a head of sales and like any good head of sales candidate, people were trying to negotiate a quota system because we were growing so fast. And we thought, okay, maybe we had grown really fast until that point and we thought, okay, maybe doubling next year. Year would be a good target. And we said like, okay, maybe maybe we're gonna double and that would be the ote. And during, like the interviews and during negotiations, we grew maybe 50%. So we were like almost halfway there already. And then we decided, okay, this is useless. We are not doing quotas. It's impossible to predict one thing. Now we are experimenting. Maybe we can do shorter term quotas, meaning maybe quarterly or monthly quotas rather than yearly, whereas it's more predictable. You can course correct if something changes. But right now we are also not doing quotas. Everyone's getting on target earnings basically.

Yeah, at Cursor. Many of our first enterprise customers bought Cursor because their developers came to their management and they said we need this tool or in many cases they were already using it. But we are growing the sales Org a lot because there are a lot of companies where, you know, the developers can't necessarily make their own decisions about the tool, but they can still be substantially accelerated by this. And so we need to reach everyone.

Yeah, the quota system, part of the reason it's there is, you know, to incentivize, you know, the behavior that you want. And there's always good incentives and bad incentives. So it's a, it's a tricky system no matter what. And one of the benefits you have is that a lot of this, there's so much demand that a lot of this for all three of you is really filling the demand versus generating it. But in getting the right team members early on that have the share the same incentives, like that is probably the prerequisite to saying we don't need a system around this because we're all aligned, we have the right DNA and culture and we're all aligned in just making this thing successful and our customers successful. And so I'm curious for each of you, are there any things, whether it's small tactics or questions or ways that you've assessed people not just for go to market but even for R and D or any area that says hey, like this is a person that would be a good fit at cursor or a good fit at anthropic and might actually, you know, not be a good fit at OpenAI?

Yeah, I'm happy to start. This space is crazy. And so across both technical and non technical orgs, what we really want to understand in their interview process is how people think through hard problems and how they balance the different trade offs. And so that's the number one thing within our interview process that we're really making sure that we understand on a candidate level. And then we also ask non technical questions just to understand a person's motivations, what they like to do generally. And then in addition to that, as part of it, you know, coming back to the question on how do we hire people without a quota on the go to market side, we want to make sure that people are really bought into our mission and really believe that we need to be building strong LLMs that are very safe. And so layering in questions in the interview process to make sure that people are willing to and excited to contribute to that mission.

On the sales side at cursor we have a very technical sales team. And that's, that's partly because it's a technical product. And so it's helpful when the salesperson understands that well, but it's also because there's a lot of ways the sales process.