Can $1 billion turn startup Scale AI into an AI data juggernaut?
The data-management company’s field chief technology officer, Vijay Karunamurthy, details its AI strategy in a world where data is becoming king.
Scale AI, an artificial-intelligence startup focused on data, raised a $1 billion venture round from prominent investors in late May, elevating the company in the hypercompetitive AI race. But with a nearly $14 billion valuation, expectations are high for the eight-year-old company.
Scale—and its investors—are betting that it can grow beyond being a tool to help customers ready their data for AI and become a software platform that plays a deeper role for them to build their own custom AI.
Scale’s latest round was led by venture firm Accel, and involved additional commitments from investors including Y Combinator, Founders Fund and Tiger Global Management.
New strategic investors included Cisco Investments, Amazon.com and the venture arms of chip companies Qualcomm, Intel and Advanced Micro Devices. Existing investor Nvidia, the semiconductor giant, also joined the round for the San Francisco-based startup.
Field Chief Technology Officer Vijay Karunamurthy spoke with The Wall Street Journal at the Collision conference in Toronto last week about Scale’s ambitions and how it will deploy its new war chest in the AI arms race. The interview has been edited for length and clarity.
WSJ: You’ve talked about being a platform that enables “artificial general intelligence," or AGI, where a machine can learn and think like a human. What does it mean for Scale AI, and how do you work with AI labs such as OpenAI and Anthropic?
Karunamurthy: We really have changed our role to being the ‘data foundry for AGI.’ This is a journey we’re going to be on for the next couple of years. We work pretty much across a range of all the large [AI] research labs, at some level or another. We’re starting to see a lot of interest from them, not just in specific capabilities, but how can you get a model that generally reasons about the world and answers questions reliably at the level like a human being can be trusted? That’s a really huge impact on society. As the research labs try to keep their eye on the ball, we are also keeping our eye on the ball.
WSJ: Why is data so important for advancing generative AI, AGI and other future technologies?
Karunamurthy: As those models get more powerful and can store more knowledge and reasoning, the amount of data it takes to saturate that model increases exponentially. That data needs to be diverse. We often now are being asked to power sophisticated data sets about how the world around us works. How do you answer a question about this table? Will the coffee stay in this cup if it tilts at a certain angle? That’s really important if we’re going to get robots to interact with the world around us. But the data is still lacking. Even if you believe we’re still far off from AGI, there’s still going to be advancements in the next few years with embodied agents able to use robotic arms or hands around a physical environment.
WSJ: What’s unique about your recent $1 billion fundraise?
Karunamurthy: We wanted to add more strategic investors. All their customers are asking about the data side of the equation. The [venture investments that companies are making] are concentrated around how they can improve their enterprise product offerings. I met with some of our more strategic investors to figure out how we’ll work with them. When you talk to Intel or Cisco, there’s a huge focus on security and trust that their private knowledge that’s fine-tuning those models doesn’t leave the bounds of its enterprise. They want to be able to audit and have strict access control over how those models are accessed.
WSJ: Scale has fresh capital to work with. How will that get deployed throughout the company?
Karunamurthy: We’re hiring across the board—growing [Scale’s full-time employees] over 20% year-over-year. We’re growing internationally as well. We just announced London is our first official international office. We know there’s a lot of talent in London, and Europe in general has a huge range of AI talent, so some of the funding is going to that expansion, too. A lot of our funding is helping build the human side of the equation and the model side of the equation. There’s a hybrid role to play with both humans and technology.
