Meta is systematically converting its internal operations into what amounts to a sprawling post-training environment for artificial intelligence models.
What post-training actually means, and why it matters
Building an AI model happens in two major phases. Pre-training is the part where you feed a model enormous amounts of data so it learns patterns, language, and reasoning. Post-training is what happens next: the fine-tuning, the alignment, the feedback loops that turn a smart-but-raw model into something actually useful.
Meta is treating its entire corporate machinery as a living laboratory for that second phase. Internal programs like “AI Week” are designed to get employees across the company actively engaging with AI tools and projects, generating real-world feedback.
When thousands of employees interact with AI systems during their actual work, whether that’s ad targeting, content moderation, product design, or internal communications, every interaction becomes a data point. Every correction becomes a training signal. Every workflow becomes a benchmark.
