Silicon Valley bets big on 'environments' to train AI agents | TechCrunch

"For years, Big Tech CEOs have touted visions of AI agents that can autonomously use software applications to complete tasks for people. But take today's consumer AI agents out for a spin, whether it's OpenAI's ChatGPT Agent or Perplexity's Comet, and you'll quickly realize how limited the technology still is. Making AI agents more robust may take a new set of techniques that the industry is still discovering. One of those techniques is carefully simulating workspaces where agents can be trained on multi-step tasks - known as reinforcement learning (RL) environments."

"AI researchers, founders, and investors tell TechCrunch that leading AI labs are now demanding more RL environments, and there's no shortage of startups hoping to supply them. "All the big AI labs are building RL environments in-house," said Jennifer Li, general partner at Andreessen Horowitz, in an interview with TechCrunch. "But as you can imagine, creating these datasets is very complex, so AI labs are also looking at third party vendors that can create high quality environments and evaluations. Everyone is looking at this space.""

"The push for RL environments has minted a new class of well-funded startups, such as Mechanize Work and Prime Intellect, that aim to lead the space. Meanwhile, large data-labeling companies like Mercor and Surge say they're investing more in RL environments to keep pace with the industry's shifts from static datasets to interactive simulations. The major labs are considering investing heavily too: according to The Information, leaders at Anthropic have discussed spending more than $1 billion on RL environments over the next year."

Big Tech visions of autonomous AI agents remain constrained by current consumer agent capabilities, revealing a need for new techniques. Reinforcement learning (RL) environments simulate workspaces where agents can be trained on multi-step tasks, emerging as a critical development component. Leading AI labs are building in-house RL environments while also courting third-party vendors due to the complexity of creating high-quality simulations and evaluations. A new wave of startups and established data-labeling firms are investing in RL environments, and major labs are considering large-scale spending, indicating a shift from static datasets toward interactive simulation infrastructure.

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