Q: Not a question but a comment:
• Jevons Paradox: Historically, making a resource more efficient actually increases total demand. If AI inference becomes 8x cheaper and faster, companies will deploy it in 10x more places, ultimately requiring more total memory.
• Inference vs. Training: TurboQuant primarily affects the inference phase (running the model). It doesn't reduce the massive amount of memory needed for the training phase, where Micron's HBM3E/HBM4 is most critical.
Most analysts (including those at Morgan Stanley and Wells Fargo) view this as a healthy evolution. It solves a bottleneck that was making AI too expensive to scale. By making AI cheaper to run, Google is actually ensuring the "AI Supercycle" lasts longer, even if it removes some of the "scarcity premium" from memory prices
• Jevons Paradox: Historically, making a resource more efficient actually increases total demand. If AI inference becomes 8x cheaper and faster, companies will deploy it in 10x more places, ultimately requiring more total memory.
• Inference vs. Training: TurboQuant primarily affects the inference phase (running the model). It doesn't reduce the massive amount of memory needed for the training phase, where Micron's HBM3E/HBM4 is most critical.
Most analysts (including those at Morgan Stanley and Wells Fargo) view this as a healthy evolution. It solves a bottleneck that was making AI too expensive to scale. By making AI cheaper to run, Google is actually ensuring the "AI Supercycle" lasts longer, even if it removes some of the "scarcity premium" from memory prices