Models are a product of their data: founded in 2016, Scale AI plays a crucial role in converting massive amounts of raw data into valuable assets for AI developers. Supporting 90% of model builders, including OpenAI, Anthropic, and Microsoft, Scale AI provides high-quality, cutting-edge frontier data—a key component in the pursuit of Artificial General Intelligence (AGI). As the demand for data in AI training escalates, Scale AI's role becomes increasingly vital. Scale AI enables companies to create the best models with the best data, having tripled its annual recurring revenue (ARR) in 2023 and projected to reach $1.4 billion in ARR by the end of 2024.
AI’s three fundamental pillars: AI is built on three essential pillars: data, compute, and algorithms. Scale AI solves the data pillar of the ecosystem, fueling the entire AI development lifecycle. It excels at transforming giant pile of raw, messy data into invaluable assets for AI developers, powering every major AI breakthrough since its inception.
The premier AI data foundry: with 90% of model builders, including OpenAI, Anthropic, Microsoft, Nvidia, Amazon, and Meta, relying on its data engine, Scale AI transforms vast amounts of raw data into high-quality datasets, resulting in superior AI performance. What sets Scale AI apart is its unmatched suite of products that go beyond basic labelling, featuring advanced algorithms and a robust infrastructure that ensures unparalleled accuracy, data fine-tuning, reinforcement learning from human feedback, safety evaluations and economies of scale.
Massive opportunity in AI data infrastructure: high-quality, fine-tuned data enables AI models to scale and forms the essential foundation upon which they are built. "Models trained in the next year are going to cost about $1 billion," Anthropic CEO Dario Amodei told an outlet. "By 2025 or 2026, we might see costs go to $5 billion or $10 billion, and it could even reach $100 billion." As models grow, their data and compute needs will escalate, with total spending on data training seeing incremental increases. This immense opportunity, with potential long-term investments reaching into the trillions, positions Scale AI at the heart of this growth.
The path from GPT-4 to GPT-10: the demand to understand data within private enterprises and consumer datasets far exceeds the scale of current frontier LLMs. For example, GPT-4 is trained on 1 petabyte of data, whereas JPMorgan alone has 150 petabytes of proprietary data. We are just beginning to tap into the potential of data training, and high-quality data is the key path that will lead us from GPT-4 to GPT-10. Scale AI is at the forefront of this development, enabling smarter self-driving cars, better healthcare AI, and more. As the foundation of AI development, Scale AI is not only shaping the future of AI but also paving the way towards AGI. This journey is significant and exciting to be part of.