This month, we turn ten. What started as an idea in a college dorm room has grown into one of the defining companies behind the most consequential technology of our time. Scale was built on a belief that was unpopular: data matters as much as models and compute. Early on, most AI systems were built on top of data that was incomplete, inconsistent, and impossible to properly validate. There was no real infrastructure for it, and no clear way to ensure quality. Scale bet on data being the thing that makes models actually work. That wasn't an obvious bet in 2016, but it turned out to be the right one.
In the early days, that meant doing work most of the industry thought was unglamorous: labeling data, creating data to capture edge cases, red-teaming to make sure models were safe, and building pipelines from scratch. There wasn't a playbook for this, but we started building the infrastructure for AI before that was a category people recognized. We trained AI to see, helped it become smarter and more capable, and created tools to measure its performance. What drove everyone, even through lean times, was the conviction that if AI was ever going to work in the real world, someone had to roll up their sleeves and do the hard work.
We started working with Waymo eight years before you could ride one to the office, because we believed in the future of self-driving. We partnered with OpenAI on InstructGPT, the predecessor to ChatGPT and the first time a language model could reliably follow instructions, a full year before ChatGPT took the world by storm. We chose to work with the Pentagon in 2020, before defense tech was a popular choice, because we believed the stakes were too high to sit on the sidelines. Most major frontier models today were originally built on the data, human feedback, and evaluation infrastructure that Scale spent a decade perfecting.
Over ten years, we've made our mark on the frontier: 15 billion human decisions applied to train the world's leading models, AI developed across 150 languages and locales, and over $1 billion paid to the contributors who made it possible.
That track record is what positions us for what comes next.
AI is moving into every domain that matters: medicine, education, law, defense, finance, public services. These are not places where you get to guess or take shortcuts. They require AI applications that work reliably, every single time. That's why we've taken everything we've learned from training models - their strengths and weaknesses, how to turn them into applications, how to evaluate them - and applied it to the hardest problems inside enterprises and governments. That infrastructure is now inside the institutions where the stakes are highest: hospitals where misdiagnosis costs lives, financial institutions where bad calls cost billions, and national security environments where the margin for error is zero.
Scale was never just about labeling data. It was about building AI that verifiably works. Our mission is to develop reliable AI systems for the world's most important decisions.
To our employees past and present, the 700+ customers we've served, investors, and contributors around the world, thank you.
The reliability race is on, and we’re building the team to meet the moment. Join us for the next ten years.
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