Arena AI hits $100M run-rate in 8 Months

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Arena AI has just hit a $100 million run rate. This is only eight months after they launched paid evaluations. I want to break down what Arena does, why they're special, why they're unique, and why they're growing so fast. Palantir is tapping in videos, Nemotron open models for US government AI. This is interesting, and a lot of drama is behind this story as well. Elizabeth Warren and Scanalon are reviving a bill to ban AI firms from selling health data. We'll get into the details on that. Flexion Robotics is training hundreds of humanoids to run office errands autonomously. And China's CXMT is landing a $3 billion memory supply deal with Tencent. We know memory is one of the critical pieces of the AI infrastructure buildout, and there's a lot of issues going on with memory increasing the costs of basically all electronics today. If you've ever been using an AI model like Claude and have been frustrated that it doesn't create images or audio or video, I'd love for you to try the MCP connector for AI Box. That's my own startup. We essentially allow you with one link, give it to Claude, and it can bring any of the AI models that you use for everything else into Claude. So you can use ChatGPT's image generation inside of Claude. You can use 11 Labs audio generation inside of Claude, or Google VO3's video generation inside of Claude. So all of the capabilities of the different AI models, there's 80 different ones that we allow you to connect. You can bring them all inside of Claude or ChatGPT or Gemini or Cursor or any of your other places where you really do all of your work, all of your workspaces with AI. So if you want to check that out, it's AIBox.ai.mcp.
It's a super easy MCP connector. So you give Claude this link inside of the connectors, and you log in to your AI Box account, and now you have all of these capabilities. I have a whole website where I explain how this works, how you can do it, and it is $8.99 a month to get started with it. So it's super cheap, and I hope that really unlocks a lot of creativity for you to be able to get 80 different AI models and capabilities inside of Cloud or whatever else you are building with. Okay, let's talk about what's going on with Arena. This is a company that is now at $100 million.
They were originally created in UC Berkeley, and it's basically an AI leaderboard, right? Like this is the company where you can go and test different AI models against each other. They've hit this $100 million annual revenue, and this is eight months after they launched their paid evaluations just back in September. This is triple what they were doing in January, which was $30 million, which honestly, even in January, I thought this was really impressive. So the company is now directly competing with Scale AI and Merkur for post-training dollars because they're selling Labs Structured Analytics built on 10 million plus human model comparisons. So basically what's going on is they've just raised $250 million total across two different rounds. So they did $150 million Series A in January at a $1.7 billion valuation. They did that from A16Z, Climber Perkins, and Felicious. So the way that they actually are making money is that the public leaderboard is still free. So essentially, if you haven't tried this before, you go to the site and it puts two different AI model responses side by side. A lot of people use this because you basically get free AI usage out of it. Instead of having to pay for Chatchity, you can go pay this and it will give you two responses side by side. You pick which one you like better, and you're helping tell it which ones are the most popular. Those leaderboards are then created so you can see, oh my gosh, Chatchity BT's new model is beating the model from Anthropic, whatever. That's where a lot of these leaderboard companies, how they run and why people use them. But how they're actually making money is that they have an AI evaluations product package which is going to show data basically to different AI labs, so like OpenAI, Anthropic, and Google. When they have their models in there getting voted on, they'll show them what areas their models are losing on to competitors. So it's like, hey, look, your model is good, but anytime a healthcare question gets asked, yours is doing poorly, or anytime a finance question, or anytime a question related to this. So they're telling them exactly where their models are lacking, and they're helping them to guide reinforcement learning. So it's a massive value for OpenAI and Google. They're going to pay a ton of money for that. It's a great value for people that get free AI usage, and also it's interesting for all of us to see what companies are doing the best in the leaderboards. So Merckers annualized revenue, which is one of their competitors, to get $1 billion this year, and Handshake AI's training army grew from $550 million to about $1 billion in three months. So I think this just kind of shows the scale of a lot of these post-training markets, how much money they're actually able to make. Let's talk about what's going on with Palantir. So they have just basically selected NVIDIA's Nemotron open models to use at the US government for all of their AI. Of course, there's a ton of drama with Anthropic and even OpenAI right now, and the US government having to pull their models and tell them, hey, you can't release your model till we give it an accurate assessment, whatever.