Scale AI, a company that provides data-labeling services for training machine learning models, has raised a $1 billion Series F round from a slew of big-name institutional and corporate investors including Amazon and Meta.

The raise, which constitutes a mix of primary and secondary funding, comes amid a boom in AI venture capital megarounds, with Amazon recently closing a $4 billion investment in OpenAI rival Anthropic, while the likes of Mistral AI and Perplexity are also in the process of raising further billion-dollar rounds at lofty valuations.

Scale AI, for its part, had already raised around $600 million in its eight year history, including a $325 million Series E round in 2021 that valued the San Francisco company at around $7 billion (double the valuation of its Series D round from the previous year) Three years on, and despite headwinds that led to a 20% workforce reduction last year, Scale AI is now valued at $13.8 billion — a sign of the times, where companies and investors are clambering over each other to get ahead in the AI gold rush.

The Series F funding round was led by Accel, which led Scale AI’s Series A round and also participated in subsequent venture rounds.

However, Scale AI has also attracted Amazon and Meta for this latest cash infusion, alongside other new investors including the venture arms of Cisco, Intel, AMD, and ServiceNow, as well as DFJ Growth, WCM, and Elad Gil. Many of its existing investors also returned, including Nvidia, Coatue, Y Combinator (YC), Index Ventures, Founders Fund, Tiger Global Management, Thrive Capital, Spark Capital, Greenoaks, Wellington Management, and former GitHub CEO Nat Friedman.

AI data

Founded in 2016, Scale AI meshes machine learning with ‘human-in-the-loop’ oversight to manage and annotate large volumes of data, which is vital for training AI systems across industries such as autonomous vehicles.

Data is, of course, the lifeblood of artificial intelligence which is why companies specializing in data management and processing are faring well right now. Just last week, Weka announced a $140 million investment at a $1.6 billion (post-money) valuation to help companies build data pipelines for their AI applications.

But most data is unstructured, making it difficult for AI systems to use this data off the bat. It needs to be labeled, which is a resource intensive endeavor especially with large data sets. Scale AI provides company with the data relevant to their applications, correctly annotated and primed for training models. It also specializes for different industries with different needs — a self-driving car startup will likely need labeled data from cameras and Lidar, whereas natural language processing (NLP) use-cases will need annotated text.

The company counts customers including Microsoft, Toyota, GM, Meta, the U.S. Department of Defense and, as of last August, ChatGPT-maker OpenAI, which is tapping Scale AI to allow companies to fine-tune its GPT-3.5 text-generating models.

With another $1 billion in the bank, Scale AI says that it’s using its fresh cash injection to help accelerate the “abundance of frontier data that will pave our road to artificial general intelligence.”

“Data abundance is not the default — it’s a choice,” Scale AI CEO and founder Alexandr Wang said in a press release. “It requires bringing together the best minds in engineering, operations, and AI. Our vision is one of data abundance, where we have the means of production to continue scaling frontier LLMs many more orders of magnitude. We should not be data-constrained in getting to GPT-10.”