Snorkel AI, a company that assists artificial intelligence (AI) labs and corporations in creating training data sets and simulated environments, has raised $350 million in a Series E funding round. This brings its total valuation to $3.5 billion, nearly triple the $1.3 billion valuation it had 17 months ago during a $100 million Series D round. Investors in this latest round include Insight Partners and S32, while existing backers like Addition, Lightspeed, Greylock, GV, and Wells Fargo also participated. Snorkel initially focused on providing software to automate the labeling of data, a critical step in training AI models. However, in the past year, the company shifted its focus to offering fully completed data sets, a service it refers to as "data-as-a-service." Unlike traditional platforms that rely solely on human experts, Snorkel uses a hybrid model that combines its software and AI models to generate synthetic data alongside input from subject matter experts. The company reports that its current annualized revenue run-rate is $375 million, an 18-fold increase from the previous year. This growth is driven by the increasing demand for high-quality training data from AI labs. Similar trends are visible among other data companies positioning themselves as AI data labs. For example, Mercor now has a gross annualized revenue of $2 billion, Handshake reached $1 billion earlier this year, and Micro1 has grown to $500 million. It's important to note that while these companies report high gross revenue figures, they typically pay 60% to 70% of their income directly to the specialists who perform the work. This means their actual net revenue is significantly lower than their headline gross numbers. Snorkel, which sells reinforcement learning environments and complete datasets rather than human labor, accounts for payments to its experts as part of its cost of goods sold, not in its annualized revenue figures. Snorkel AI launched commercially in 2019 after four years of research led by co-founder and CEO Alex Ratner and his team at Stanford's AI lab. The company's evolution from a data labeling tool to a provider of comprehensive AI training solutions reflects the growing need for high-quality, ready-to-use data in the rapidly expanding AI industry.