IBM and Meta show open AI's industrial and on-device future is real.

IBM partners with Together AI and Nvidia to launch B300 chips, marking a shift from esoteric AI to industrial-scale computing. The collaboration highlights the capital intensity and technological difficulty of deploying 10,000-GPU clusters. This move challenges neo-clouds, which may retain an edge in agility and innovation for specialized workloads. Meta's

open-source Muse Glimmer model is a 30B parameter dense model optimized for on-device inference, achieving 30-40 tokens per second on a Mac M3. It employs speculative decoding and context windowing techniques from Gemini/Gemma models. This signals a strategic pivot toward capable small models that run locally, addressing privacy and cost

concerns while enabling agentic workloads. OpenAI's blog post on Astra raises more questions than answers, framing a potential cybersecurity risk as a reason to delay release. The panel debates whether this is genuine security caution or marketing to build anticipation. The lobotomization of Claude due to overhyped capabilities serves as

a cautionary tale for overplaying safety concerns. The consensus leans toward open models as the safer long-term bet, as they democratize access to advanced AI and force closed providers to compete. Open ecosystems enable broader defense against adversaries and prevent a disparity in AI capability that could be exploited.