Scale AI

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These 50 companies exemplify the innovation, progress, and potential of AI.

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Fortune 50 AI Innovators

The essential guide to a sector that’s now attracting one out of every four startup investment dollars.

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AI automates, but to get to the sophistication of chatbots like ChatGPT requires humans—and a lot of them. While the LLMs behind OpenAI's ChatGPT or Anthropic's Claude learn the statistical connections in language on their own, to guide and refine the responses these models will provide through a chatbot interface, companies turn to a process known as reinforcement learning from human feedback (or RLHF) where human evaluators judge whether the model's answers are useful, helpful, and "safe" (usually meaning not offensive or likely to be used for harm). Then the model is rewarded for producing answers more like those that the human evaluators labelled as good. But all that requires humans. Which is where Scale comes in. Its whole business is around providing the data labels that AI companies need. Often, those labelers work for low wages in the developing world. In Kenya, for example, Scale AI, a Silicon Valley darling, paid workers between $1 and $3 to train AI models, according to The Verge. (A spokesperson said the company adheres to minimum wage law in all counties in which it operates.) Founded by Alexandr Wang in 2016, who was then only 19 years old, Scale is best known as the data maven behind many of the world’s most sophisticated AI models, including those used by a number of self-driving car companies and to support decision-making in defense. The tasks are often simple and monotonous: identify characteristics of retail products or identify objects in an image. While Scale got its start labeling objects in image data for self-driving algorithms, it has now expanded its training to a wide assortment of AI models for customers like Etsy, Instacart, Meta, and, of course, OpenAI. And it’s ridden the AI boom to raise a total of approximately $600 million at a most recent valuation of $7.3 billion. CTO Vijay Karunamurthy said, however, that Scale, which also builds its own AI algorithms, is looking to go beyond finding laypeople to more curated experts, like an authority in ancient Aramaic, to help train models: “Expert feedback is incredibly valuable to help the model structure its thinking.”

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AI adoption is accelerating across industries, but it falls apart without solid data at the foundation. As organizations grapple with complexity, unstructured data, silos and legacy systems, leaders across healthcare, retail and financial services are building stronger data foundations and using AI to support intelligent decision-making at scale. Join us to explore the strategies for transforming data from a bottleneck into a catalyst for innovation.


FROM DATA CHAOS TO AI IMPACT: WHAT INDUSTRY LEADERS ARE GETTING RIGHT

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AI is only as powerful as the stack beneath it. Compute, chips, data, and energy are becoming the decisive factors in who can build, scale, and sustain intelligent systems. But how much infrastructure will we really need—and who’s prepared to run it? As build-outs accelerate, operators face tough choices about capacity, efficiency, cooling, carbon, and even something as fundamental as cable management. In this session, technology and infrastructure leaders explore what it takes to keep pace with runaway demand while keeping costs and complexity in check.

The New Power Stack: Building the Infrastructure of Intelligence

The defining quandary for today’s Fortune 500 CEOs: how do you lead a global business in an anti-global age? In an era of rising protectionism, fractured supply chains, and deepening geopolitical rivalries, the old CEO playbook–emphasizing scale, efficiency, and integration—is being rewritten. The new imperatives: resilience, localization, and national interest. How can CEOs lead organizations with operations in multiple markets when the world is turning inward? And should they even try?

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