Scale AI is the unseen utility company of the artificial intelligence boom. If a major tech firm needs data to train its models, it usually goes to Scale. For years, that data was mostly text and images—static snapshots of the world used to teach machines how to speak and see. But in the last six months, the order form has changed. The dominant request from the biggest labs is no longer “teach this machine to read.” It is “teach this machine to act.” This quiet pivot in the supply chain suggests the industry is hitting a wall with its current methods and is scrambling to build software that does more than just chat.
Key Takeaways
- Reinforcement learning now accounts for over 50% of Scale AI’s training workload.
- Scale AI provides training data for DeepMind, Meta, and Apple.
- Reinforcement learning requires more compute than previous AI training paradigms.
Chetan Rane, a product head at Scale AI, says that reinforcement learning now makes up more than half of the work they do. Six months ago, that number was less than a quarter. This is a significant reallocation of resources for a company that feeds data to giants like DeepMind, Meta, and Apple.
The industry is moving away from “self-supervised learning”—where a model looks at billions of sentences to predict the next word—toward a method designed to help computers achieve specific goals. The labs are no longer satisfied with models that are merely book-smart; they want models that can navigate the messy reality of the internet.
The big deal
For the average person, this shift marks the difference between a chatbot and an agent. Current AI models are excellent at answering questions or writing poems, but they are generally terrible at completing multi-step tasks like booking a flight or filing a tax return. They get confused, lose track of the goal, or hallucinate details.
By pivoting to reinforcement learning, companies are trying to fix this reliability gap. They want to build software that can be trusted to execute actions online, not just generate text. If this works, AI becomes less of a search engine replacement and more of a digital employee that can handle administrative drudgery.
How it works
Traditional AI training is like reading every cookbook in the library. You might know the theory of how to bake a soufflé, and you can describe the process perfectly, but you have never actually held a whisk or turned on an oven.
Reinforcement learning is different. It is like putting a chef in a kitchen and letting them try to bake the soufflé 1,000 times. If the soufflé collapses, they get zero points. If it rises, they get a reward. Over time, through trial and error, they learn exactly how to move their hands to get the result.
To do this for AI, engineers build “environments”—simulations of real-world websites or software. They set the AI loose in a simulated banking site or travel portal. When the model successfully navigates the user flow to reach the goal, that successful pathway is reinforced in the model’s digital wiring. It learns by doing, rather than by reading.
The catch
This method is brittle. Rane admits that while the models get smarter at specific tasks, they struggle to “generalize.” If you train a model to navigate a specific airline website, and the airline changes its font or moves the “Book Now” button, the model might fail because the scenario doesn’t match its training. It hasn’t truly learned the concept of booking; it has learned a specific routine.
It is also expensive. Running these millions of trial-and-error simulations requires massive amounts of computing power. Rane notes that reinforcement learning demands much more compute than previous training methods. This means the cost of building these models is likely going up, not down.
What to watch
Expect the AI market to fracture into specialists. Rane observes that labs are already diverging: some are going all-in on coding agents, others on enterprise work, and others on consumer tasks. We are moving away from the idea of a single “God model” that does everything perfectly.
Keep an eye on:
- Specialized releases: Look for models marketed specifically for “agents” or “actions” rather than general chat.
- Infrastructure costs: Since this method is compute-heavy, cloud costs for these companies will remain high.
- Brittleness: If you use these new agents, watch how they handle website updates. If a site redesign breaks your AI assistant, this training limitation is why.
