Human Resources is undergoing a fundamental shift from a reactive cost center to a proactive driver of value. Yet many organizations remain anchored in a minimalist approach to people analytics. While generative AI and autonomous agents are gaining traction across the enterprise, HR’s use of data is still often limited to basic turnover prediction.

Red Hat built a $34 billion business on Linux. IBM bought it. What the deal validated was a hypothesis that had held for four decades: that companies extracting enormous value from shared code would, in self-interest, keep funding the projects they depended on. That hypothesis is now under stress. Not because anyone decided to stop funding open source. Because the industry that funded it most — SaaS — is being dismantled by the industry that depends on it most — AI.

In a previous piece, I explored how eight independent research teams converged on the same insight: instead of building memory systems around the model, train the model itself to manage memory as a learned skill. Post-memory training — using reinforcement learning in the post-training phase — produces agents that decide what to store, delete, consolidate, and retrieve, all optimized against task completion.