The era of cautious experimentation with neural networks has come to an end. It has been replaced by aggressive physical expansion, where raw computing power collides with the necessity for rigorous architectural control. According to Jack Clark in the Import AI report dated November 24, 2025, the industry has shifted toward planning data centers with a 2GW capacity. For C-suite executives, the signal is clear: capital expenditures are no longer just about purchasing licenses; they are about reserving colossal physical infrastructure.
This race is triggering a "balkanization" of technology. Geopolitical risks and regulatory concerns are forcing market leaders to choose between technological dependency and investing in their own proprietary hardware bases.
Simultaneously, the very nature of AI agents is evolving. A group of researchers from top-tier universities (including MIT, UIUC, CMU, USC, UVA, and Berkeley) has introduced OSGym—a framework that takes neural networks beyond the browser and directly into the operating system. According to the creators of OSGym, this infrastructure allows for the management of over a thousand parallel OS instances. Such an agent is capable of executing end-to-end business processes—for example, editing an image in a graphic editor and autonomously uploading it to a third-party application. OSGym provides a standardized operational cycle: Configure, Reset, Operate (via keyboard and mouse emulation), and Evaluate.
From an economic perspective, this approach demonstrates phenomenal efficiency. Researchers proved that running 1,024 OS copies to test agents across more than 200 tasks cost a mere $43. Maintaining a single OS instance costs between $0.20 and $0.30 per day. This makes the training and auditing of systems capable of working outside the browser accessible even to academic groups and small startups.
However, the transition to agents with OS-level access raises acute questions regarding security and control. If an agent can click, type, and navigate between programs, the need for a protected environment becomes paramount. Using OSGym in isolated, reproducible conditions allows for a detailed analysis of algorithmic behavior prior to deployment.
The cost of infrastructure for AI agents has effectively dropped to the price of a cup of coffee. In this landscape, the advantage will go to those who invest early in evaluation and training tools. Organizations that fail to create their own "testing grounds" to adapt AI to the specifics of their internal operations will inevitably be overtaken by competitors who are already training systems to work with any piece of enterprise software.