The Future of Agentic Computing (www.cjroth.com)

Recent advancements in specialized computing chips for AI inference may revolutionize the AI landscape. As companies like Etched, Groq, and Taalas invest in custom silicon designed specifically for large language models (LLMs), the drive for greater efficiency and speed is becoming evident. Taalas's innovative approach permanently encodes model weights into chip wiring, achieving speeds of 15,000–17,000 tokens per second while drastically reducing power consumption and costs. This shift towards dedicated hardware promises to make agentic workloads — tasks demanding rapid iterative reasoning — faster, cheaper, and more energy-efficient, potentially reshaping how AI is deployed across various platforms. The implications extend beyond performance, influencing key aspects of the software stack, including the push towards local-first architectures. As inference speeds increase, the latency introduced by network calls to remote databases becomes a critical bottleneck, urging developers to explore solutions like co-located databases. The focus on local data not only enhances speed but also introduces a layer of privacy by keeping sensitive information under user control. If these trends continue, we could see a paradigm shift towards accessible, fast, and private AI, fundamentally changing how and where AI operates, making it an integral part of everyday technology.