Scale AI, which provides data labeling services to companies looking to train machine learning models, has raised $1 billion in Series F funding from a number of prominent institutional and corporate investors, including Amazon and Meta.
The funding is a combination of primary and secondary funding and is the latest in a series of large venture capital investments in AI. Amazon recently completed a $4 billion investment in OpenAI rival Anthropic, and companies like Mistral AI and Perplexity are also in the process of raising billions more at high valuations.
Prior to this round, Scale AI had raised about $600 million in its eight-year history, including a $325 million Series E in 2021 at a valuation of about $7 billion, double its Series D valuation in 2020. Scale AI is now valued at $13.8 billion after facing headwinds from laying off 20% of its workforce last year, a sign of the times as investors scramble to get ahead in the AI gold rush.
The Series F was led by Accel, which also led the Series A and participated in subsequent venture rounds.
In addition to Amazon and Meta, Scale AI attracted a variety of new investors. The venture arms of Cisco, Intel, AMD, and ServiceNow participated, as well as DFJ Growth, WCM, and investor Elad Gil. Many 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 .
Betting on the growing importance of data
Data is the lifeblood of artificial intelligence, which is why companies specializing in data management and processing are thriving right now. Last week, Weka announced that he had raised $140 million at a post-funding valuation of $1.6 billion to help companies build data pipelines for his AI applications. Announced.
Founded in 2016, Scale AI combines machine learning and “human-involved” monitoring to manage and annotate large amounts of data essential to training AI systems across industries such as self-driving cars. Masu.
However, most data is unstructured, making it difficult for AI systems to use such data immediately. It needs to be labeled, which is a resource-intensive task, especially for large datasets. Scale AI provides enterprises with data that is properly annotated and ready for model training. We also specialize in different industries with different needs. A self-driving car company will likely need labeled data from cameras or his lidar, while natural language processing (NLP) use cases will require annotated text.
The company's customers include Microsoft, Toyota, GM, Meta, and the U.S. Department of Defense, and as of August last year, ChatGPT maker OpenAI is using Scale AI to help companies fine-tune their GPT-3.5 text generation models. That's what I do.
Scale AI says it will use the new funding to accelerate “cutting-edge data enrichment that paves the way to artificial general intelligence.”
“Data abundance is a choice, not a default,” said Scale AI CEO and founder Alexandr Wang in a press release. “We need to bring together the best talent in engineering, operations, and AI. Our vision is data abundance, to have the means of production to continue to scale the state of the art LLMs by many more orders of magnitude. We should not be data constrained to get to GPT-10.”