Today we’re announcing Mechanize, a startup focused on developing virtual work environments, benchmarks, and training data that will enable the full automation of the economy. We will achieve this by creating simulated environments and evaluations that capture the full scope of.

xAI is probably the first to spend as much compute on RL as on pretraining. The easy gains from shifting compute to RL are now gone. With this arbitrage closed RL scaling will slow. Progress will now come from the quality and realism of RL environments rather than mere scaling.

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The future is building software, not curating static datasets. Today’s AI systems learn best by interacting with digital environments, attempting tasks, and learning from outcomes. This demands dedicated, full-time specialists with strong expertise, not outsourced contractors.

Previously, AI progress relied heavily on monotonous, low-skill labeling from third-party contractors producing basic text, visual, and audio data at scale. But models have outgrown simple tasks, demanding richer context and deeper expertise. The era of sweatshop data is over.

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We call this approach replication training: tasking AI models with exactly replicating existing software using clear specs and references. This trains models to read precisely, execute reliably, and demonstrate resilience in complex, long-horizon tasks.

Imagine if pretraining a language model meant manually creating the entire training corpus. Clearly, this would be impractical. Instead, we leverage vast content available online. We expect RL will follow a similar path, replicating abundant existing software as RL tasks.

Beyond this point, we expect models trained via RL to acquire powerful, task-agnostic, few-shot capabilities on tasks that currently require painstaking, task-specific training, just as GPT-3 unlocked few-shot capabilities for language.

How much bigger must RL get to have a GPT-3 moment?. We expect this will soon require roughly 10,000 years of cumulative human-equivalent task time, comparable to GTA V or major operating systems.

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Replication tasks target critical skills current models lack: accurately interpreting instructions, reliably recovering from mistakes, and sustaining precise execution on tasks that humans take months to complete, moving us closer to reliable, capable AI agents.

Replication training naturally extends existing AI trends by building tasks directly from abundant human-generated data already available online.

We are proposing a new AI paradigm called replication training: tasking AIs to precisely replicate existing software. Abundant internet text unlocked powerful language models. Similarly, abundant software available today will enable massive-scale, efficient RL training.

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Before GPT-3, achieving good performance required specialized fine-tuning for each task. Today's RL is similar: models need to be carefully trained to handle tasks like deep research, web search, or coding. But we think RL will soon have its GPT-3 moment.

RT @tamaybes: Most AI labs talk about merely “augmenting” humans at work. They say this because AI currently falls short, not out of some d….

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Software engineering tasks might be among the earliest automated, yet fully replacing software engineers will happen surprisingly late, likely after AI takes over a large share of white-collar jobs.

AI-generated code continues a decades-long climb toward greater abstraction, from compilers to frameworks. Automation shifts what engineers do long before it erases the role.

AI will soon write most lines of code. But just as compilers didn’t eliminate software engineers, this new wave of automation won't erase the role immediately.