Scale AI – Powering Data Centricity for AI

Written by
Arvind Ayyala

“Data is the new oil” – a familiar commentary we have heard over the last decade, but it stands true that it is still the single most important variable in the age of AI. Generative AI (“GenAI”) adoption is being propelled by building better infrastructure at the model layer as well as the increasing velocity of new GenAI enterprise applications. Underpinning the adoption and powering the build-out of AI infrastructure and applications requires a data engine — we believe Scale AI is the category-defining leader. 

To support their mission of building the most data-centric AI platform, we are pleased to announce our participation in Scale AI’s $1B Series F financing round. 

AI is forecasted to drive ~$7tn in global economic growth over 10 years, which is underpinned by an estimated 1.5% annual productivity uplift. This has implications for a massive uptick in the demand for the underlying GenAI solutions (i.e. software/services), a market that is expected to reach $143B by 2027, with a 5-year CAGR of 73%. Scale is positioned as a category leader that can benefit from these tailwinds, which translate to the need for deploying performant foundational models and enterprise-grade AI applications. Scale’s platform combines software and services and has created a positive network effect in the still early innings of AI-led business models – utility of the platform by more customers has accrued more high-quality training data across varying domains, making it efficient, performant and cost-effective for customers to revert to Scale with new use cases. 

Scale’s data-centric AI platform has evolved from being a trusted vendor providing an API for training data (initially focused on addressing autonomous vehicle use cases) to managing the entire lifecycle of AI development across industry verticals. The platform’s utility will be accentuated as organizations leverage the solution to develop AI copilots, process data across modalities, and deploy agentic approaches. This product evolution and the buildout of the customer network effect have been aided by two self-reinforcing factors: 1) a robust software offering that is in itself a proxy for the evolving enterprise AI infrastructure stack; 2) a distributed tech-enabled workforce that supports the RLHF (reinforcement learning from human feedback) aspects of building and deploying AI safely and securely. 

The Scale team has been steadfast in its focus on establishing the “data pillar” that advances AI by fueling the entire AI model development lifecycle. We see tremendous growth potential for Scale AI’s offerings in Japan and are proud to support Scale in its mission to deliver the most data-centric AI platform.