Matt Brockman

We hit an annualized run rate of $120M in October. This year alone, we’re projected to add around $150M in net new revenue. This is an amazing milestone and a testament to the work of the @e2b team. I’m particularly excited about two things: 1/ This showcases how strong the tailwinds of AGL (Agent-Led Growth) can be. If you build a tool for agents and optimize your whole growth engine to serve them, you can see tremendous growth. This isn’t something you can just throw money at and buy. Agents don’t decide based on ads. You need a sophisticated growth engine that fine-tunes things like your AEO. I think this is what all future B2B devtool sales will look like. Increasingly, decisions about tool adoption will be made by agents, not humans. 2/ The use cases we’re serving. The majority of our revenue and usage comes from agents running in production. Not RL. When we started, we had two theses: (i) code is all you need—all agents will use code to complete tasks (which has turned out to be true); and (ii) sandboxes for agentic workloads will be a bigger market than sandboxes for RL workloads, but takeoff will be slower (still playing out). In the past 30 years, every company has become a software company. In the next 10 years, every company will have become an agent company. We’re building an infrastructure layer to power all these agents.

It's been quite a ride

Arena started in 2024 as a UC Berkeley Sky Computing Lab project for comparing models head to head. Today, it's grown into a platform for evaluating model performance across agents, text, code, images, and videos, running up to 600,000 @e2b sandboxes a day. Each sandbox is an isolated cloud computer where an agent can write code, install dependencies, and work for hours on coding, research, reports, and presentations. That isolation keeps results trustworthy and secure: no session's code or files can reach another's and skew the comparison. That security at scale gets tested every time a frontier model drops. Before GPT-5 went public, people rushed to Code Arena to try it first. "When GPT-5 was about to come out, a lot of people came to Code Arena to experience the model firsthand because it wasn't out to the public yet. E2B was the backbone behind all of that. So when we had this massive surge, we didn't have to worry about whether we could handle it." - Aryan Vichare, Founding Engineer, @arena Read the full case study: e2b.dev/customers/arena Watch the case study video: youtube.com/watch?v=vBKHCiZP…

Introducing E2B Embed: it brings AI agent sandboxes directly into your product and run them on infrastructure you control. E2B Embed packages the full E2B stack on a single node, giving you the flexibility to deploy in your own environment or a customer’s. Open source under Apache 2.0, it supports Docker Compose, Terraform on GCP, and Kubernetes. All with the same E2B SDK, CLI, and API. youtube.com/watch?v=w-7eAIZC…

Today, we are announcing our Series A and a new product: @raindrop_ai Simulations. We've now raised $50m from @CRV and @lightspeedvp to protect the world from agent failures, big and small.

“We can’t slow down because China will beat us.” We can slow down. Chinese AI is waterskiing behind the US. Their AI capabilities come at a delay since they mainly come from distilling US AIs. If we slow down, they slow down. Next, we can propose to cooperate and make this more robust with onsite inspectors. “We will slow down if you do too.” This is incentive-compatible for China. If both sides are slowed by roughly the same factor, while the risk of losing control of our AIs is reduced, then this does not really harm China’s competitiveness but it makes them safer. It’s incentive-compatible, and @elonmusk is possibly best positioned to lead US-China negotiations. Then, if China refuses to slowdown and safeguard their future AIs, there are a variety of measures we could take. To get an agreement, the US can apply typical forms of negotiation pressure. The US could also disrupt their AIs (e.g., backdoor models by poisoning pretraining data, many other possibilities at aibetrayal.com) that could interfere with their AI development or deployment if they were uncooperative. Begin with unilateral restraint, then propose to cooperate. If that fails, consider measures to counteract them. A US-China slowdown is possible.

Your sandbox, your call. Bring your own sandbox or connect a sandbox provider. Choose the environment that fits your workload: • CPU, GPU, and memory options • Fully managed environments or deployments within your VPC • File and secret storage options We offer first-class integrations with @blaxelAI, @CloudflareDev, @daytonaio, @digitalocean, @e2b, @modal, @OracleCloud, @RunloopAI, and @vercel.

We’re introducing Box Mount. Mount a Box folder into a sandbox like @e2b and keep it in two-way sync. The agent reads and writes Box content with standard file operations as if they were local. Every operation flows through the Box API so governance applies automatically, no custom policy code required. 2 commands to get started: agent-mount config agent-mount mount "/mnt/box" "<box-id>" Developer Preview is now open.

Paper Instruments benchmarked sandbox providers for their RL rollouts, and E2B's tool execution came in up to 3x faster than every other sandbox provider they tested, reducing idle GPU time and training cost. @paperinstr trains frontier models for knowledge work (consulting, finance, banking, law), running thousands of concurrent GRPO rollouts, each needing the solver and grader fully isolated from one another to prevent reward hacking. Every rollout also needs to start from the exact same state in order to reflect the policy being trained accurately. It also needs to boot and execute fast, since the GPU sits idle waiting on the rollout to finish. With @e2b, Paper Instruments isolates each rollout in a microVM, builds the initial state from a template, then snapshots the running sandbox, letting thousands of rollouts launch straight from the snapshot instead of booting cold, so they get consistency and speed while reducing training cost. Read the full case study: e2b.dev/blog/paperinstrument…