Cursor Pulls Unexpected $2.3B Round Amid AI Frenzy

0:00Welcome to the podcast. I'm your host, Jaden Schaefer. Today on the podcast, we're talking about Cursor, which is a coding assistant, and it has just completed an absolutely astronomical.

0:30by the Wall Street Journal. And what's insane is this actually more than doubles the company's previous valuation, which was$9.9 billion. It did this when it raised$900 million in a Series C in June. When that happened, everyone was pretty shocked and, you know, blown away that they were able to raise almost a billion dollars. And now, just five months later, they're at$2.3 billion raised. This most recent fundraise was led by, well, it was actually co-led. There's two leads on it. Acel was one of them, who is an existing investor, they put money in. And then there was Coat, which is new on the cap table.

1:06They have some strategic investors, including Nvidia, of course, and Google, who also joined in the round. No shocker here, Nvidia is basically handing out money to the top AI use cases, as they know the money is going to come straight back to them as more compute will be needed. So Joshua Kushner, who's from Thrive Capital, led the company's two prior rounds that they did. And he was also participating in this round as well. The co-founder and CEO is Michael Truel. He was talking to the Wall Street Journal recently, and he told them that all the money they've just raised from this round is going to be put towards developing Composer, which is basically their new AI model.

1:47This was released back in October. And so now they are trying to improve this. What's impressive here is so often with these AI companies, they get into this tricky situation where they're reliant on either open AI or anthropic, and they use that for their foundational code model. They're kind of wrapped around it. And if there's any sort of politics or issues with those foundational code models, it can be really tricky. In addition, they have to pay a ton of their revenue to those code models to help. So the fact that they've actually developed their own AI model is obviously why they're able to raise so much money.

2:21It's the number one coding tool, Cursor is. And so because there's just so many users that are paying so much money to these third-party AI vendors, by making their own tool, this makes a lot of sense. Next year could be a really interesting one for Cursor. The company is growing very quickly. OpenAI and Anthropic are both really getting into coding products. OpenAI has their own tool and Claude Code from Anthropic is obviously, you know, one of the preferred tools by all developers. So this is getting really competitive. But that's not to say cursor won't be able to keep up up and up until this point, they have one of the largest user bases of coding developers of any other company.

3:04In the most recent numbers, I could find cursor had over 1 million daily users and they have 10s of 1000s of enterprises, they have opening eye, Instacart Salesforce, all of them are using cursor, which is interesting because OpenAI has their own, you know, coding tool, but a lot of their employees are still using cursor. The user base includes a whole bunch of different individuals and teams, they have subscription tiers for pro and business plans. So they got a bunch of different options. One thing that I do find very interesting with cursor is the fact that their coding tool is actually built on top of Microsoft's open source VS code editor.

3:42So they have essentially kind of grabbed that open source project and built their own tool. One of the LLMs embedded in their editor, like I mentioned, is called Composer. This just came out in October. And according to them, it's a mixture of experts algorithm, right? So that basically means that when there is a question, it sends it to a number of quote unquote experts inside of the AI model that each determine which can answer the question best. They also oftentimes will send the question to multiple sort of like fine-tuned models inside, and then they'll collaborate and see who came up with what answer, and they'll like come up with an answer that's kind of a consolidation of all of those.

4:22This all happens in the background very quickly, so you're not gonna notice it. You just ask a question, it will give you a response, but that's how it's working. It's an algorithm. It runs four times faster than LLMs with comparable output quality, according to them. So it can complete a lot of coding tasks in under 30 seconds. So the reason why I think a lot of people are getting excited and they're able to continue you raise so much money is because yes, you could go use something like, you know, Cloud Code. Yes, you could go use OpenAI's product. But by using them, if you actually can get stuff done faster, a lot of people love that because I know for us, we use Cloud Code at AI Box, which by the way, if you don't have it, go check out AIbox.ai.

4:57You can access to all of the top AI models in one place for 20 bucks a month. But we use Cloud Code a lot to help us. It does take a very long time when we launch it on a task or a new project, it can sit there for 10 or 15 minutes working on the code base. So if you could bump that up four times faster, it does make a really big difference. We know, of course, like as a refresher, an LLM is comprised of a bunch of different kernels. So these are paralyzed snippets of code. They are all running across a whole bunch of graphics card cores at once. And developers, they're usually going to be writing kernels with the help of what's called a CUDA library.

5:34It's, you know, it's basically abstracts away some of the complexity that is associated with this. They have these kind of prepackaged code that removes the need to write everything from scratch. What's interesting here is cursor says that they did not use any CUDA libraries while they were building Composer. And they said that they implemented the models kernels using PXT. This is kind of a what's called a low level machine language. NVIDIA chips are using this a lot. And that approach apparently has helped cursor achieve more than 3x performance increase across a bunch of their components. So they've kind of built this own thing in house, you can see, because they're using NVIDIA chips to run this, you can see why NVIDIA would get in on this most recent round and give them a lot of funding for this.

6:19NVIDIA is definitely licking their chops and sees this as an amazing opportunity to continue their lead and dominance in the chip space. So it's going to be or I guess in the GPU space. So it's going to be interesting to see how that plays out. In any case, thank you so much for tuning into the podcast today. If you learned anything new, it would mean the world to me if you could leave a rating and review on the podcast. A ton is happening. I will try my best to keep you up to date on everything happening in AI news. Hope you guys all have a great rest of your day and I'll catch you in the next episode.