The November Thoughtworks Technology Radar highlights AI's dominance, but managing agentic AI at scale presents new challenges. Key enterprise concerns include sharing context, controlling token costs, managing cognitive load, ensuring security, and building robust platforms. These issues are interconnected and require integrated strategies, always remembering AI should serve human benefit.Ben Thompson's Stratechery newsletter notes recent bipartisan opposition to new data centers, tied to the perception of AI. This opposition, shifting from internet to AI focus, benefits existing data center owners through reduced supply and increased token costs. Non-US entities and companies pushing AI to edge devices, like Apple, may also gain. Established businesses might quietly favor regulations that hinder new data center construction, acting as barriers to competition.Hadley Wickham investigates whether AI token costs are subsidized, concluding that per-token API prices are mostly unsubsidized, but subscription plans show some subsidy. Research into "Weight-Space Programming" explores the ability of models to understand and manipulate other models' weights. This raises the exciting possibility of directly downloading capabilities into models.Jessica Kerr suggests a shift from teaching AI to letting AI teach humans. Houck et al. developed the CAFE(S) framework—Clarity, Actionability, Fidelity, Efficiency, Security—to assess the quality of context provided to AI agents. Finally, industry experience shows that a significant portion of AI token spend goes into verifying model outputs and implementing guardrails, rather than initial generation, indicating the importance of robust verification processes.