Where the bottleneck is shifting
Where the bottleneck is shifting
Every major advance in model capability has changed the scarce unit of training data.
Early models needed large volumes of annotated examples. Data-labeling companies met that demand at scale.
As models became more capable, they needed richer signals about what constituted a good outcome. Expert networks organized domain specialists to create and judge high-skill work.
When models began learning to act in the real world, they needed training grounds to learn how to use tools, make decisions, and learn from outcomes. Environment vendors began building RL environments that replicated familiar enterprise software.
Now, the bottleneck is shifting once again. Models can now use apps and complete discrete tasks, but they still struggle with stateful, long-horizon, policy-constrained workflows, which is what most economically-valuable digital work actually looks like.
Training this capability requires enterprise-grounded learning systems that capture the complexities and context of real work: intent, permissions, business rules, exceptions, corrections, and outcomes.
Every major advance in model capability has changed the scarce unit of training data.
Early models needed large volumes of annotated examples. Data-labeling companies met that demand at scale.
As models became more capable, they needed richer signals about what constituted a good outcome. Expert networks organized domain specialists to create and judge high-skill work.
When models began learning to act in the real world, they needed training grounds to learn how to use tools, make decisions, and learn from outcomes. Environment vendors began building RL environments that replicated familiar enterprise software.
Now, the bottleneck is shifting once again. Models can now use apps and complete discrete tasks, but they still struggle with stateful, long-horizon, policy-constrained workflows, which is what most economically-valuable digital work actually looks like.
Training this capability requires enterprise-grounded learning systems that capture the complexities and context of real work: intent, permissions, business rules, exceptions, corrections, and outcomes.
Hue partners with enterprises to capture operational data that explains how valuable work actually gets done, then transforms that data into realistic environments, challenging tasksets, and verifiable rewards that models can learn from.
Most environments begin with simulated software, synthetic data, and expert-authored tasks. Hue’s environments capture the nuances of real work that models struggle with: cross-system dependencies, business rules, permissions, exceptions, and consequences.
Hue partners with enterprises to capture operational data that explains how valuable work actually gets done, then transforms that data into realistic environments, challenging tasksets, and verifiable rewards that models can learn from.
Most environments begin with simulated software, synthetic data, and expert-authored tasks. Hue’s environments capture the nuances of real work that models struggle with: cross-system dependencies, business rules, permissions, exceptions, and consequences.