What is Firecrawl?

**Greg Isenberg** (0:00)
This episode is the clearest explanation of Firecrawl on the internet and how you can use it to build a real business that makes you real money. Firecrawl feels like giving your AI eyes. Right now, AI is smart, but it's blind. It can't see the internet, it can't go to a website, it can't grab data. So Firecrawl fixes that. Once you see it in action, it changes how you think about building products, how you think about collecting data, and how you think about what's possible with AI. In this episode, I break down what Firecrawl actually is, how it plays into your AI stack, and walk you through a bunch of startup ideas that you can make money from it. I used firecrawl with ideabrowser.com, and I reached out to them to ask them to sponsor this video. They said yes, so that more people can see this, get the sauce and build and make money with it. If Firecrawl has been on your radar, and you just want a clear explanation of what it is and how you can use it as a founder, then this episode is for you. And if you've never heard of it, honestly, that's even better, because what I'm about to show you is going to change how you think about what you can build with AI and where the next 12 months of building is going. Let's get into it.
By the end of the episode, you're gonna understand why AI is blind, why it needs hands and eyes, why Firecrawl is that, and why the people that understand how to use Firecrawl are gonna be able to create SaaS apps and software that are super, super valuable to people. I'm talking the most valuable software products are gonna be using this data scraping tool at the backbone because it makes their AI 10 times smarter. But in order to understand this, we need to take a step back. The problem is AI is blind. If you listen to this channel, you know that the more context you give to a cloud, the more context you give to a ChatGPT, the better output you're gonna get. So we know that AI models need web data. It needs top tier data to actually go and provide really good outputs. Why does this matter now? Well, it matters because if you think about the first era of AI, that was the Trapbot era. Chat GPT just came out in 2022 It answers questions. It was cool, but pretty limited. Then we entered the Copilot era. You know, cursor, GitHub, Copilot. It was faster, but you still needed to drive. It was you, the human being, that was doing it. We've now entered this AI agent era. AI is doing the work for you. Things like cloud code. It browsers, it researches, it builds. But it still needs the data. And Firecrawl is how you're going to get that data. This is often called the computer use era. We now have AI agents that can see and control computers. In the past, it was human beings, right? We bought mouses and keyboards, and we had human beings actually going and clicking and doing things, right? That's, you know, gonna be the minority, as weird as it is to say that. You have tools like Perplexity Computer, OpenAI, had Operator came out about a year ago. AI browsers the web for you. You know, GPT 5.4 beats humans at computer tasks. You know, Clode has its computer use API, screenshots and clicks, it's got full desktop control. Manus was the one who was one of the first to do that. You have browser use, which is an open source. You know, so all these computer uses, all these AI agents that are going and doing things, well, what do they all need? Well, they need clean web data, and that's Firecrawl. And the reason I got interested in Firecrawl is because I built ideabrowser.com, and ideabrowser.com is a place where you have trends and the best startup ideas on the planet. And I needed the data, I needed the trend data, and we built on top of Firecrawl to actually go and get some of that data. Now we have the number one startup ideas and trends product on the planet, and it's all been cut in largely part that we're using tools like Firecrawl to actually go and get that data. What most people don't get about this whole era that we're in is they think that AI is just chat bots that answer questions, they think web scrapers are illegal and shady, they think you need to code everything yourself, they think data is free and easy to get, and they think that web scraping is a thing for developers. But what actually is happening is AI agents are doing work autonomously. Web data is critical AI infrastructure, literally critical. One API call replaces thousands of lines, and clean structured data is the new oil. By the end of this episode, I think you're going to agree by that. So the people that understand how important the clean data is and how important you can use the clean data and wrap it around a brain, an LLM, and wrap that around a piece of software, those are the people that are going to be able to create the most valuable startups in the next 12 months. And I think that the people who understand that have a 12 month head start. And that's why I wanted to make this episode. Traditional scraping versus new scrapers like Firecrawl. Let's just talk about that so we can understand what the difference is. The old way of scraping was you wrote a custom scraper per site, you managed proxies and browsers, you handled anti-bot detection, you had to parse messy HTML manually, the scripts would break when site changes, this happened all the time. Basically it was a massive headache. Now you just do one API call, you get clean data back in seconds, it could work on any site, or I think like 99% or 98%, some high 90% of sites, and the AI handles layout changes. The way I think about my agent stack is that every builder, if you're listening to this, you're probably going to need five different layers. You're going to need an agent harness. That's going to be something like a cloud code, cursor, codecs, or idea browser pro. You're going to need something that basically is handling all the different agents in one place. Then you're going to need something like a search layer. Something that's going to go and search different things. Perplexity has a good MCP, EXA as well. Then you're going to need a web data layer. That's what we're talking about today in this episode. You're going to use Firecrawl for scraping, browsing, and extraction. Basically, the web data layer, your agents need to see the internet. You're going to need to be able to see the internet, to see the data, in order to provide value back in the form of a startup and software. You're going to need an Ops brain. I recently did an episode, I encourage you to listen to it if you haven't already, around Obsidian and Claude Cote. I don't care if you use Notion, I don't care if you use Apple Notes, but you're going to need some brain for storing your meeting notes, storing your contacts, and you can use something like Notion or Obsidian. Then you're going to have to have some outbound and audience stack as well, something like an Instantly and Apollo. If people are interested, I can spend more time and do a whole separate episode on some of these tools. But today we're going to be talking about the Firecrawl web data layer. So what is it? What is Firecrawl? What is the clearest way to understand it? You put in a website, goes through the Firecrawl API, and you get back a clean markdown, a structured JSON, some screenshots. And you can feed that to any AI model. That's it. Simple as that. We don't need to overthink about it. Think it. The way I think about it is Firecrawl has six superpowers. You can scrape. So you can go and scrape one page to a clean markdown. So something like scrape one blog post from, you know, gregisenberg.com/blogpost.